From 9bb6d5d1a54db024976afe47346d9336d078a80c Mon Sep 17 00:00:00 2001 From: Ruby Zhu <76190371+RubyZ10@users.noreply.github.com> Date: Thu, 26 Oct 2023 12:45:44 -0700 Subject: [PATCH] Localized file check-in by OneLocBuild Task: Build definition ID 21368: Build ID 107831016 (#2393) --- libs/localization/src/lib/en.cs.json | 14 +- libs/localization/src/lib/en.de.json | 14 +- libs/localization/src/lib/en.es.json | 602 ++++++++++----------- libs/localization/src/lib/en.fr.json | 10 +- libs/localization/src/lib/en.hu.json | 14 +- libs/localization/src/lib/en.it.json | 606 ++++++++++----------- libs/localization/src/lib/en.ja.json | 610 +++++++++++----------- libs/localization/src/lib/en.ko.json | 14 +- libs/localization/src/lib/en.nl.json | 598 ++++++++++----------- libs/localization/src/lib/en.pl.json | 610 +++++++++++----------- libs/localization/src/lib/en.pt-BR.json | 604 ++++++++++----------- libs/localization/src/lib/en.pt-PT.json | 14 +- libs/localization/src/lib/en.ru.json | 610 +++++++++++----------- libs/localization/src/lib/en.sv.json | 14 +- libs/localization/src/lib/en.tr.json | 610 +++++++++++----------- libs/localization/src/lib/en.zh-Hans.json | 610 +++++++++++----------- libs/localization/src/lib/en.zh-Hant.json | 14 +- 17 files changed, 2801 insertions(+), 2767 deletions(-) diff --git a/libs/localization/src/lib/en.cs.json b/libs/localization/src/lib/en.cs.json index 73e88fe441..e24e246f94 100644 --- a/libs/localization/src/lib/en.cs.json +++ b/libs/localization/src/lib/en.cs.json @@ -906,6 +906,8 @@ "index": "Index", "output": "Výstup", "predictedY": "Predikované Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Pravděpodobnost : {0}", "trueY": "Skutečné Y", "xValue": "Hodnota X:", @@ -1147,7 +1149,7 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Řídicí panel interpretovatelnosti", - "importantWords": "Show most important words", + "importantWords": "Zobrazit nejdůležitější slova", "topFeatureList": "Analýza seznamu hlavních funkcí", "allButton": "VŠECHNY FUNKCE", "negButton": "NEGATIVNÍ FUNKCE", @@ -1162,7 +1164,7 @@ "trueAnswer": "Skutečná odpověď: ", "inputs": "Vstupy", "outputs": "Výstupy", - "sliderAriaLabel": "Slider for most important words" + "sliderAriaLabel": "Posuvník pro nejdůležitější slova" }, "Legend": { "featureLegend": "LEGENDA TEXTOVÉ FUNKCE", @@ -1195,8 +1197,8 @@ "columnTwo": "Index", "columnThree": "Skutečné Y", "columnFour": "Predikované Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Správně", + "columnFourOD": "Nesprávně", "columnFive": "Další metadata", "chooseObject": "Zvolte zjištěný objekt", "examples": "Příklady", @@ -1216,8 +1218,8 @@ "panelInformation": "Informace", "predictedLabel": "Predikovaný popisek", "predictedY": "Předpověď: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", + "correctDetections": "Správná zjištění: ", + "incorrectDetections": "Nesprávná zjištění: ", "prefix": "Objekt: ", "rows": "Řádky: ", "search": "Hledat", diff --git a/libs/localization/src/lib/en.de.json b/libs/localization/src/lib/en.de.json index 4abbce4051..e1e17dca63 100644 --- a/libs/localization/src/lib/en.de.json +++ b/libs/localization/src/lib/en.de.json @@ -906,6 +906,8 @@ "index": "Index", "output": "Ausgabe", "predictedY": "Vorhersage für Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Wahrscheinlichkeit : {0}", "trueY": "TRUE Y", "xValue": "X-Wert:", @@ -1147,7 +1149,7 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Interpretierbarkeitsdashboard", - "importantWords": "Show most important words", + "importantWords": "Wichtigste Wörter anzeigen", "topFeatureList": "Wichtigste Featurelistenanalysen", "allButton": "ALLE FEATURES", "negButton": "NEGATIVE FEATURES", @@ -1162,7 +1164,7 @@ "trueAnswer": "Richtige Antwort: ", "inputs": "Eingaben", "outputs": "Ausgaben", - "sliderAriaLabel": "Slider for most important words" + "sliderAriaLabel": "Schieberegler für die wichtigsten Wörter" }, "Legend": { "featureLegend": "TEXTFUNKTIONSLEGENDE", @@ -1195,8 +1197,8 @@ "columnTwo": "Index", "columnThree": "WAHR Y", "columnFour": "Vorhergesagtes Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Richtig", + "columnFourOD": "Fehlerhaft", "columnFive": "Andere Metadaten", "chooseObject": "Erkanntes Objekt auswählen", "examples": "Beispiele", @@ -1216,8 +1218,8 @@ "panelInformation": "Informationen", "predictedLabel": "Vorhergesagte Bezeichnung", "predictedY": "Vorhergesagt: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", + "correctDetections": "Richtige Erkennungen: ", + "incorrectDetections": "Falsche Erkennungen: ", "prefix": "Objekt: ", "rows": "Zeilen: ", "search": "Suchen", diff --git a/libs/localization/src/lib/en.es.json b/libs/localization/src/lib/en.es.json index 2e70834537..bccd34f4cc 100644 --- a/libs/localization/src/lib/en.es.json +++ b/libs/localization/src/lib/en.es.json @@ -3,26 +3,26 @@ "close": "Cerrar", "tooltipButton": "Botón de información sobre herramientas", "identityFeature": "Característica de identidad", - "infoTitle": "Additional information", - "spinButton": "Spin", - "editButton": "Edit", - "decreaseValue": "Decrease value", - "increaseValue": "Increase value", - "decreaseValueByOne": "Decrease value by 1", - "increaseValueByOne": "Increase value by 1", - "loading": "Loading..." + "infoTitle": "Información adicional", + "spinButton": "Girar", + "editButton": "Editar", + "decreaseValue": "Disminuir valor", + "increaseValue": "Incrementar valor", + "decreaseValueByOne": "Disminuir valor en 1", + "increaseValueByOne": "Incrementar valor en 1", + "loading": "Cargando…" }, "ChartContextMenu": { - "hideData": "Hide data table", - "viewData": "View data table", - "viewInFullScreen": "View in full screen", - "printChart": "Print chart", - "downloadCSV": "Download CSV", - "downloadPNG": "Download PNG image", - "downloadJPEG": "Download JPEG image", - "downloadPDF": "Download PDF document", - "downloadSVG": "Download SVG vector image", - "downloadXLS": "Download XLS" + "hideData": "Ocultar la tabla de datos", + "viewData": "Ver tabla de datos", + "viewInFullScreen": "Ver en pantalla completa", + "printChart": "Imprimir gráfica", + "downloadCSV": "Descargar CSV", + "downloadPNG": "Descargar imagen PNG", + "downloadJPEG": "Descargar imagen JPEG", + "downloadPDF": "Descargar documento PDF", + "downloadSVG": "Descargar imagen de vector SVG", + "downloadXLS": "Descargar XLS" }, "CausalAnalysis": { "AggregateView": { @@ -39,7 +39,7 @@ "description": "El análisis causal responde a preguntas «qué pasaría si» sobre cómo habrían cambiado los resultados reales en diferentes opciones de directiva, como una estrategia de precios diferente para un producto o un tratamiento alternativo para un paciente. A diferencia de las predicciones de modelos que identifican patrones de correlación importantes, estas herramientas le ayudan a identificar las características causales más importantes que afectan directamente al resultado de interés. Estos modelos identifican el efecto causal de una característica (normalmente denominado «tratamiento»), manteniendo constantes otras características de confusión. Para obtener mejores resultados, asegúrese de que el conjunto de datos completo contiene todas las características disponibles que pueden correlacionarse con el resultado como confusores.", "directAggregate": "Efecto causal agregado directo de cada tratamiento con intervalo de confianza del 95 %", "here": "aquí", - "infoTitle": "Additional information on aggregated causal effects", + "infoTitle": "Información adicional sobre los efectos causales agregados", "lasso": "Un lazo (o regresión logística si y es binario) era adecuado para predecir y a partir de X[-i], y un lazo (o regresión logística si X[i] es categórico) era adecuado para predecir X[i] a partir de Χ [-i]. El efecto causal se puede ver como la correlación media de los valores residuales y la variación sorpresa de las dos tareas de predicción. Más información sobre el aprendizaje automático doble", "unconfounding": "¿Qué son las características de confusión?" }, @@ -51,7 +51,7 @@ "description": "Los efectos causales individuales pueden informar de las intervenciones personalizadas, como una promoción dirigida a los clientes o un plan de tratamiento individualizado. ¿Cómo respondería un individuo con un conjunto determinado de características a un cambio en una característica causal o tratamiento? La herramienta causas hipotética calcula los cambios marginales en los resultados reales de una persona determinada si cambia su nivel de tratamiento. Este análisis le permite comprender cómo habrían cambiado los resultados reales en diferentes opciones de la directiva, como una estrategia de precios diferente para un producto o un tratamiento alternativo para un paciente. Especifique el tratamiento de interés y observe cómo cambiaría el resultado real.", "directIndividual": "Efecto de tratamiento individual directo de cada tratamiento con intervalo de confianza del 95 %", "index": "Índice de punto de datos", - "infoTitle": "Additional information on individual causal what-if", + "infoTitle": "Información adicional sobre el hipóc. causal individual", "missingParameters": "Esta pestaña requiere proporcionar un conjunto de datos de evaluación.", "newOutcome": "Nuevo resultado", "selectTreatment": "Seleccionar tratamiento", @@ -85,7 +85,7 @@ "averageGainBinary": "Promedio de ganancias de establecer el tratamiento {0} en su valor de línea base {1}.", "averageGainContinuous": "Promedio de los beneficios de directivas alternativas sobre ningún tratamiento de \"{0}\".", "header": "Estas herramientas ayudan a crear directivas para futuras intervenciones. Puede identificar qué partes de la muestra experimentan las respuestas más grandes a los cambios en las características causales o los tratamientos, y crear reglas para definir qué poblaciones futuras se deben destinar a determinadas intervenciones.", - "infoTitle": "Additional information on treatment policy", + "infoTitle": "Información adicional sobre la directiva de tratamiento", "nSample": "n = {0}", "noData": "Sin datos" } @@ -116,8 +116,8 @@ "cancel": "Cancelar", "title": "Cambiar cohorte", "subText": "Seleccione un cohorte de la lista de cohortes. Aplique el cohorte al panel.", - "selectCohort": "Select a cohort", - "cohortList": "Cohort list" + "selectCohort": "Seleccionar un cohorte", + "cohortList": "Lista de cohorte" }, "PreBuiltCohort": { "featureNameNotFound": "No se encontró el nombre de la característica en el conjunto de datos", @@ -148,13 +148,13 @@ "predictedClass": "Clase prevista", "predictedValue": "Valor previsto" }, - "Size": "Size", - "loading": "Loading...", + "Size": "Tamaño", + "loading": "Cargando...", "counterfactualEx": "Ejemplo de contrafactual {0}", "counterfactualName": "Nombre de contrafáctico hipotético", "createWhatIfCounterfactual": "Crear contrafáctico hipotético", "createCounterfactual": "Contrafactual", - "revertToBubbleChart": "View bubble chart", + "revertToBubbleChart": "Ver gráfico de burbujas", "createOwn": "Cree su propia contrafactura:", "currentClass": "Clase actual", "currentRange": "Intervalo actual", @@ -167,9 +167,9 @@ "listDescription": "Esta lista muestra qué puntos de datos de la muestra de datos actual tienen la respuesta causal más grande al tratamiento seleccionado, en función de todas las características incluidas en el modelo causal estimado. Las cinco columnas izquierdas informan de si se recomienda el tratamiento para la observación, el tratamiento actual, el efecto estimado del tratamiento (efecto de aplicar un tratamiento a partir de una línea base sin tratamiento para tratamientos binarios o aumentar o disminuir la característica de tratamiento en un 10 % del tamaño de tratamiento típico en la muestra: [Dynamic: informar el cambio numérico en el tratamiento que usamos]) y los intervalos de confianza inferior y superior (CI) para este efecto. Las columnas restantes muestran el estado de tratamiento actual y otras características de cada observación.", "localImportanceDescription": "Las principales características clasificadas en la fila {0} para perturbar la predicción del modelo deseado. Basado en el análisis de hipótesis para la predicción: {1}", "localImportanceSelectData": "Seleccione un punto de datos para ver el gráfico de importancia local", - "largeLocalImportanceSelectData": "Select a bubble, followed by a data point to view local importance chart", - "localImportanceFetchError": "There was an error while fetching the local importance data. Error details: {0} Please check the data used.", - "BubbleChartFetchError": "There was an error while fetching the data. Error details: {0} Please check the data used.", + "largeLocalImportanceSelectData": "Seleccione una burbuja, seguida de un punto de datos para ver el gráfico de importancia local.", + "localImportanceFetchError": "Error al capturar los datos de importancia local. Detalles del error: {0} compruebe los datos usados.", + "BubbleChartFetchError": "Error al capturar los datos. Detalles del error: {0} compruebe los datos usados.", "noData": "Sin datos", "noFeatures": "No hay características disponibles", "panelDescription": "Explore los contrafácticos y cree los suyos propios. Busque características para ver los valores sugeridos en un conjunto diverso de ejemplos de contrafácticos. Establezca los valores contrafácticos sugeridos para la característica (para ello haga clic en el texto \"Establecer valor\" para cada nombre contrafáctico). Asigne un nombre a su contrafáctico y guárdelo.", @@ -223,13 +223,13 @@ "subText": "Más información sobre la cohorte seleccionada. Editar su nombre de cohorte. Eliminar esta corte." }, "FeatureList": { - "featureList": "Feature List", + "featureList": "Lista de características", "apply": "Aplicar", "features": "Características", "importances": "Importancias", "treeMapDescription": "Para volver a encontrar el mapa de árbol, seleccione y guarde las siguientes características. La importancia de las características se calculó usando información mutua con el error en las etiquetas verdaderas. Puede usarla como guía de aprendizaje sobre el mapa de árbol.", "staticTreeMapDescription": "Vea las características que se usaron para entrenar el mapa de árbol. Las importancias de las características se calcularon usando información mutua con el error en las etiquetas verdaderas.", - "searchResultMessage": "Results displayed out of {resultLength} for {searchValue}" + "searchResultMessage": "Resultados mostrados de {resultLength} para {searchValue}" }, "TreeViewParameters": { "maximumDepth": "Profundidad máxima", @@ -295,7 +295,7 @@ "disabledWarning": "El mapa térmico de errores está deshabilitado a menos que se cambie la cohorte global para representar \"Todos los datos\" debido al mapa térmico que se genera para el conjunto de datos completo. Vuelva al conjunto de datos completo para ver el mapa térmico de errores." }, "MatrixSummary": { - "heatMapInfoTitle": "Additional information on heat map", + "heatMapInfoTitle": "Información adicional sobre el mapa térmico", "heatMapDescription": "Con el mapa térmico, puede centrarse en filtros de características interseccionales específicos y calcular las tasas de error desagregadas. Comience con dos características del conjunto de datos para comparar.", "heatMapStaticDescription": "Con el mapa térmico, puede centrarse en filtros de características interseccionales específicos y calcular las tasas de error desagregadas. Se deben seleccionar hasta dos características para crear un mapa térmico a través del SDK antes de ver el panel." }, @@ -311,108 +311,108 @@ }, "Metrics": { "AccuracyScore": { - "Name": "Accuracy score", - "Info": "The accuracy score represents the ratio of correct to total instances in the data.", - "Short": "Accuracy", - "Title": "Additional information on accuracy score" + "Name": "Puntuación de precisión", + "Info": "La puntuación de precisión representa la relación entre las instancias correctas y totales de los datos.", + "Short": "Precisión", + "Title": "Información adicional sobre la puntuación de precisión" }, "ErrorRate": { - "Name": "Error rate", - "Info": "The error rate represents the percentage of instances in the node for which the system has failed.", - "Short": "Error rate", - "Title": "Additional information on error rate" + "Name": "Calificación de errores", + "Info": "La calificación de errores representa el porcentaje de instancias en el nodo para las que sistema ha producido errores.", + "Short": "Calificación de errores", + "Title": "Información adicional sobre calificación de errores" }, "F1Score": { - "Name": "F1 score", - "Info": "The F1 score is the harmonic mean of the precision and recall metrics.", - "Short": "F1 score", - "Title": "Additional information on F1 score" + "Name": "Puntuación F1", + "Info": "La puntuación F1 es la media más importante de las métricas de precisión y recuperación.", + "Short": "Puntuación F1", + "Title": "Información adicional sobre la puntuación F1" }, "MeanAbsoluteError": { - "Name": "Mean absolute error", - "Info": "The mean absolute error is the average of the sum of the errors.", - "Short": "Mean abs. error", - "Title": "Additional information on mean absolute error" + "Name": "Error absoluto medio", + "Info": "El error absoluto medio es el promedio de la suma de los errores.", + "Short": "Error abs. medio", + "Title": "Información adicional sobre el error absoluto medio" }, "MeanSquaredError": { - "Name": "Mean squared error", - "Info": "The mean squared error is the average of the squares of the errors.", - "Short": "Mean sq. error", - "Title": "Additional information on mean squared error" + "Name": "Error cuadrático medio", + "Info": "El error cuadrático medio es el promedio de los cuadrados de los errores.", + "Short": "Error cuadrático medio", + "Title": "Información adicional sobre el error cuadrático medio" }, "Precision": { - "Name": "Precision score", - "Info": "The precision is the ratio of true positives over all predicted positives.", - "Short": "Precision", - "Title": "Additional information on precision" + "Name": "Puntuación de precisión", + "Info": "La precisión es la proporción de verdaderos positivos sobre todos los positivos previstos.", + "Short": "Precisión", + "Title": "Información adicional sobre la precisión" }, "Recall": { - "Name": "Recall score", - "Info": "The recall is the ratio of true positives over all actual positives.", - "Short": "Recall", - "Title": "Additional information on recall" + "Name": "Puntuación de coincidencia", + "Info": "La coincidencia es la proporción de verdaderos positivos con respecto a todos los positivos reales.", + "Short": "Coincidencia", + "Title": "Información adicional sobre la coincidencia" }, "MacroPrecision": { - "Name": "Macro averaged precision score", - "Info": "The macro averaged precision is the ratio of true positives over all predicted positives computed independently per class and averaged.", - "Short": "Macro precision", - "Title": "Additional information on macro averaged precision" + "Name": "Puntuación de precisión media macro", + "Info": "La precisión media macro es la proporción de los verdaderos positivos con respecto a todos los positivos previstos calculados independientemente por clase y promediados.", + "Short": "Precisión macro", + "Title": "Información adicional sobre la precisión media macro" }, "MicroPrecision": { - "Name": "Micro averaged precision score", - "Info": "The micro averaged precision is the ratio of true positives over all predicted positives aggregated for all classes.", - "Short": "Micro precision", - "Title": "Additional information on micro averaged precision" + "Name": "Puntuación de precisión media micro", + "Info": "La precisión media micro es la proporción de los verdaderos positivos con respecto a todos los positivos previstos agregados para todas las clases.", + "Short": "Precisión micro", + "Title": "Información adicional sobre la precisión media micro" }, "MacroRecall": { - "Name": "Macro averaged recall score", - "Info": "The macro averaged recall is the ratio of true positives over all actual positives computed independently per class and averaged.", - "Short": "Macro recall", - "Title": "Additional information on macro averaged recall" + "Name": "Puntuación de coincidencia media macro", + "Info": "La coincidencia media macro es la proporción de los verdaderos positivos con respecto a todos los positivos reales calculados independientemente por clase y promediados.", + "Short": "Coincidencia macro", + "Title": "Información adicional sobre la coincidencia media macro" }, "MicroRecall": { - "Name": "Micro averaged recall score", - "Info": "The micro averaged recall is the ratio of true positives over all actual positives aggregated for all classes.", - "Short": "Micro recall", - "Title": "Additional information on micro averaged recall" + "Name": "Puntuación de coincidencia media micro", + "Info": "El promedio de coincidencia micro es la relación de los verdaderos positivos sobre todos los positivos reales agregados para todas las clases.", + "Short": "Coincidencia micro", + "Title": "Información adicional sobre la coincidencia media micro" }, "MacroF1Score": { - "Name": "Macro averaged F1 score", - "Info": "The macro averaged F1 score is the harmonic mean of the macro averaged precision and recall metrics.", - "Short": "Macro F1 score", - "Title": "Additional information on macro averaged F1 score" + "Name": "Puntuación F1 media macro", + "Info": "La puntuación F1 macromediada es la media armónica de las métricas de precisión y recuperación macromediadas.", + "Short": "Puntuación F1 macro", + "Title": "Información adicional sobre la puntuación F1 media macro" }, "MicroF1Score": { - "Name": "Micro averaged F1 score", - "Info": "The micro averaged F1 score is the harmonic mean of the micro averaged precision and recall metrics.", - "Short": "Micro F1 score", - "Title": "Additional information on micro averaged F1 score" + "Name": "Puntuación F1 media micro", + "Info": "La puntuación F1 micromediada es la media armónica de las métricas de precisión y recuperación micromediadas.", + "Short": "Puntuación F1 micro", + "Title": "Información adicional sobre la puntuación F1 media micro" }, "MeanAveragePrecision": { - "Name": "Mean average precision score", - "Info": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall.", - "Short": "Mean avg precision", - "Title": "Additional information on mean average precision score" + "Name": "Puntuación de la media de la precisión promedio", + "Info": "La precisión media para los modelos de detección de objetos es el promedio de AP (precisión media) en todas las clases. Esto evalúa la solidez del modelo de detección de objetos y encapsula el equilibrio entre precisión y recuperación.", + "Short": "Media de la precisión promedio", + "Title": "Información adicional sobre la puntuación de la media de la precisión promedio" }, "AveragePrecision": { - "Name": "Average precision score", - "Info": "Average precision for object detection models is calculated for a selected class.", - "Short": "Avg precision", - "Title": "Additional information on average precision score" + "Name": "Puntuación de la precisión promedio", + "Info": "La precisión media de los modelos de detección de objetos se calcula para una clase seleccionada.", + "Short": "Precisión promedio", + "Title": "Información adicional sobre la puntación de la precisión promedio" }, "AverageRecall": { - "Name": "Average recall score", - "Info": "Average recall for object detection models is calculated for a selected class.", - "Short": "Avg recall", - "Title": "Additional information on average recall score" + "Name": "Puntuación de la coincidencia promedio", + "Info": "La recuperación media de los modelos de detección de objetos se calcula para una clase seleccionada.", + "Short": "Coincidencia promedio", + "Title": "Información adicional sobre la puntuación de la coincidencia promedio" }, "metricName": "Nombre de la métrica", "metricValue": "Valor de métrica" }, "MetricSelector": { "selectorLabel": "Seleccionar métrica", - "feature1SelectorLabel": "Rows: Feature 1", - "feature2SelectorLabel": "Columns: Feature 2" + "feature1SelectorLabel": "Filas: Característica 1", + "feature2SelectorLabel": "Columnas: Característica 2" }, "Navigation": { "cohortSaved": "Se ha guardado la nueva cohorte. Vea la lista de cohortes en Configuración de cohortes.", @@ -433,9 +433,9 @@ "defaultLabelCopy": "Copia de todos los datos" }, "TreeView": { - "ariaLabel": "Interactive chart", - "disabledArialLabel": "Disabled interactive chart", - "treeMapInfoTitle": "Additional information on tree map", + "ariaLabel": "Gráfico interactivo", + "disabledArialLabel": "Gráfico interactivo deshabilitado", + "treeMapInfoTitle": "Información adicional sobre el mapa de árbol", "treeDescription": "La visualización de árbol usa la información mutua entre cada característica y el error para separar mejor las instancias de error de las instancias correctas jerárquicamente en los datos. Esto simplifica el proceso de detectar y resaltar patrones de error comunes. Para encontrar patrones de error importantes, busque nodos con un color rojo más fuerte (es decir, una alta tasa de errores) y una línea de relleno más alta (es decir, una cobertura de errores alta). Para editar la lista de características que se usan en el árbol, haga clic en \"Lista de características.\" Usar el menú desplegable \"seleccionar métrica\" para obtener más información sobre el rendimiento de los nodos de error y éxito. Tenga en cuenta que esta selección de métrica no afectará a la forma en que se genera el árbol de errores.", "treeStaticDescription": "La visualización de árbol usa la información mutua entre cada característica y el error para separar mejor las instancias de error de las instancias correctas jerárquicamente en los datos. Esto simplifica el proceso de detectar y resaltar patrones de error comunes. Para encontrar patrones de error importantes, busque nodos con un color rojo más fuerte (es decir, una alta tasa de errores) y una línea de relleno más alta (es decir, una cobertura de errores alta). Para ver la lista de características que se usaron para crear este árbol de errores, haga clic en \"Lista de características.\" Usar el menú desplegable \"seleccionar métrica\" para obtener más información sobre el rendimiento de los nodos de error y éxito. Tenga en cuenta que esta selección de métrica no afectará a la forma en que se genera el árbol de errores.", "disabledWarning": "El mapa de árbol de error está deshabilitado a menos que se cambie la cohorte global para representar \"Todos los datos\" debido al mapa de árbol que se genera para el conjunto de datos completo. Vuelva al conjunto de datos completo para ver el mapa de árbol de errores." @@ -770,7 +770,7 @@ "countHelperText": "Un histograma del número de puntos", "ditherLabel": "Debe interpolar", "groupByCohort": "Agrupar por cohorte", - "logarithmicScaling": "Enable logarithmic scaling", + "logarithmicScaling": "Habilitación del escalado logarítmico", "numOfBins": "Número de discretizaciones", "selectClass": "Seleccionar clase", "selectFeature": "Seleccionar característica", @@ -794,7 +794,7 @@ "importancePrefix": "Importancia", "numberOfDatapoints": "Número de puntos de datos", "rowIndex": "Índice de fila", - "absoluteIndex": "Absolute index", + "absoluteIndex": "Índice absoluto", "xValue": "Valor X", "yValue": "Valor Y" }, @@ -822,11 +822,11 @@ }, "CohortEditor": { "columns": { - "index": "Index", - "dataset": "Dataset", - "predictedY": "Predicted Y", - "trueY": "True Y", - "classificationOutcome": "Classification outcome", + "index": "Índice", + "dataset": "Conjunto de datos", + "predictedY": "Eje Y previsto", + "trueY": "Y verdadero", + "classificationOutcome": "Clasificación de resultado", "regressionError": "Error" }, "TreatAsCategorical": "Tratar como valor categórico", @@ -852,8 +852,8 @@ "save": "Guardar", "saveAndSwitch": "Guardar y cambiar", "selectFilter": "Seleccionar filtro", - "noFiltersApplied": "No filters applied", - "filterAdded": "Filter added" + "noFiltersApplied": "No se han aplicado filtros", + "filterAdded": "filtro agregado" }, "Columns": { "classificationOutcome": "Clasificación de resultado", @@ -863,8 +863,8 @@ "falsePositive": "Falso positivo", "none": "Recuento", "predictedProbabilities": "Probabilidades de predicción", - "predictedLabels": "Predicted labels", - "trueLabels": "True labels", + "predictedLabels": "Etiquetas previstas", + "trueLabels": "Etiquetas verdaderas", "regressionError": "Error de regresión", "trueNegative": "Verdadero negativo", "truePositive": "Verdadero positivo", @@ -885,7 +885,7 @@ "aggregatePlots": "Trazados agregados", "chartType": "Tipo de gráfico", "colorValue": "Valor del color", - "infoTitle": "Additional information on data analysis chart view", + "infoTitle": "Información adicional sobre la vista del gráfico de análisis de datos", "helperText": "Cree cohortes de conjunto de datos para analizar las estadísticas del conjunto de datos a lo largo de filtros como el resultado previsto, las características del conjunto de datos y los grupos de errores. Obtenga información sobre la sobrepresentación y la infrapresentación en el conjunto de datos.", "individualDatapoints": "Puntos de datos individuales", "missingParameters": "Esta pestaña requiere proporcionar un conjunto de datos de evaluación.", @@ -906,6 +906,8 @@ "index": "Índice", "output": "Resultado", "predictedY": "Eje Y previsto", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Probabilidad : {0}", "trueY": "Y verdadero", "xValue": "Valor de X:", @@ -974,10 +976,10 @@ "dependencePlotHelperText": "Este trazado de dependencia muestra la relación de los valores de una característica con sus valores de importancia de la característica correspondiente.", "dependencePlotTitle": "Trazados de dependencia", "helperText": "Explore las principales características importantes que afectan a las predicciones generales del modelo (también conocidas como explicación global). Use el control deslizante para mostrar las importancias descendentes de las características. Todas las importancias de características de cohortes se muestran en paralelo y se pueden desactivar seleccionando la cohorte en la leyenda. Haga clic en cualquiera de las características del gráfico para ver un trazado de densidad a continuación de cómo afectan los valores de la característica seleccionadas a la predicción.", - "infoTitle": "Additional information on aggregate feature importance", + "infoTitle": "Información adicional sobre la importancia de las características agregadas", "legendHelpText": "Para activar o desactivar las cohortes en el trazado, haga clic en los elementos de la leyenda.", "missingParameters": "Esta pestaña requiere que se proporcione el parámetro de importancia de la característica local.", - "sortByCohort": "Sort by cohort", + "sortByCohort": "Ordenar por cohorte", "sortBy": "Ordenar por punto de datos", "topAtoB": "Principales características de {0} por su importancia", "viewDependencePlotFor": "Ver trazado de dependencia para:", @@ -1020,15 +1022,15 @@ }, "Statistics": { "accuracy": "Precisión: {0}", - "bleuScore": "Bleu score: {0}", - "bertScore": "Bert score: {0}", - "exactMatchRatio": "Exact match ratio: {0}", - "rougeScore": "Rouge Score: {0}", + "bleuScore": "Puntuación de puntuación de puntuación: {0}", + "bertScore": "Puntuación de Bert: {0}", + "exactMatchRatio": "Relación de coincidencia exacta: {0}", + "rougeScore": "Puntuación de Score: {0}", "fnr": "Tasa de falsos negativos: {0}", "fpr": "Tasa de falsos positivos: {0}", - "hammingScore": "Hamming score: {0}", + "hammingScore": "Puntuación de hamming: {0}", "meanPrediction": "Media de predicción {0}", - "meteorScore": "Meteor Score: {0}", + "meteorScore": "Puntuación de meteorología {0}", "mse": "Error cuadrático medio: {0}", "precision": "Precisión: {0}", "rSquared": "R²: {0}", @@ -1036,10 +1038,10 @@ "selectionRate": "Tasa de selección: {0}", "mae": "Error absoluto medio: {0}", "f1Score": "Puntuación F1: {0}", - "samples": "Sample size: {0}", - "meanAveragePrecision": "Mean average precision: {0}", - "averagePrecision": "Average precision: {0}", - "averageRecall": "Average recall: {0}" + "samples": "Tamaño de muestra {0}", + "meanAveragePrecision": "Precisión media promedio {0}", + "averagePrecision": "Precisión media: {0}", + "averageRecall": "Promedio de recuperación: {0}" }, "ValidationErrors": { "addFilters": "Agregar filtros", @@ -1147,30 +1149,30 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Panel de interpretabilidad", - "importantWords": "Show most important words", + "importantWords": "Mostrar las palabras más importantes", "topFeatureList": "Análisis de la lista de características principales", "allButton": "TODAS LAS CARACTERÍSTICAS", "negButton": "CARACTERÍSTICAS NEGATIVAS", "posButton": "CARACTERÍSTICAS POSITIVAS", - "legendText": "Positive scalar feature importances represent the extent that the words were important towards the classification of your selected label, and negative scalar feature importances represent words that encouraged your model away from your selected label.", - "legendTextForQA": "The left text box and the bar chart display the predictions of the model. The right text box shows the feature importance associated with a selected token. Positive feature importances represent the extent that the words were important towards marking the selected token as the starting/ending position of the answer.", + "legendText": "Las importancias escalares positivas de los rasgos representan el grado en que las palabras fueron importantes para la clasificación de su etiqueta seleccionada, y las importancias escalares negativas de los rasgos representan las palabras que animaron a su modelo a alejarse de su etiqueta seleccionada.", + "legendTextForQA": "El cuadro de texto a la izquierda y el gráfico de barras muestran las predicciones del modelo. El cuadro de texto a la derecha muestra la importancia de la característica asociada a un token seleccionado. Las importancias de las características positivas representan la medida en que las palabras fueron importantes para marcar el token seleccionado como la posición inicial o final de la respuesta.", "label": "Etiqueta", "colon": ": ", - "startingPosition": "STARTING POSITION", - "endingPosition": "ENDING POSITION", - "predictedAnswer": "Predicted answer: ", - "trueAnswer": "True answer: ", - "inputs": "Inputs", - "outputs": "Outputs", - "sliderAriaLabel": "Slider for most important words" + "startingPosition": "POSICIÓN INICIAL", + "endingPosition": "POSICIÓN FINAL", + "predictedAnswer": "Respuesta prevista: ", + "trueAnswer": "Respuesta verdadera: ", + "inputs": "Entradas", + "outputs": "Resultados", + "sliderAriaLabel": "Control deslizante de las palabras más importantes" }, "Legend": { "featureLegend": "LEYENDA DE CARACTERÍSTICA DE TEXTO", "posFeatureImportance": "IMPORTANCIA DE LA CARACTERÍSTICA POSITIVA", "negFeatureImportance": "IMPORTANCIA DE LA CARACTERÍSTICA NEGATIVA", - "cls": "CLS: start of the sentence", - "sep": "SEP: end of the sentence", - "selectedWord": "Selected word: " + "cls": "CLS: inicio de la oración", + "sep": "SEP: final de la oración", + "selectedWord": "Palabra seleccionada: " }, "BarChart": { "featureImportance": "IMPORTANCIA DE LA CARACTERÍSTICA" @@ -1178,59 +1180,59 @@ }, "InterpretVision": { "Cohort": { - "close": "Close", - "errorCohortName": "Please choose a unique cohort name.", - "errorNumSelected": "Please select at least one (1) item.", - "itemsSelectedSingular": "item selected", - "itemsSelectedPlural": "items selected", - "save": "Save cohort", - "saveAndClose": "Save and close", - "saveAndSwitch": "Save and switch", - "textField": "New cohort name", - "title": "Save new cohort" + "close": "Cerrar", + "errorCohortName": "Elija un nombre de cohorte único.", + "errorNumSelected": "Seleccione al menos un (1) elemento.", + "itemsSelectedSingular": "elemento seleccionado", + "itemsSelectedPlural": "elementos seleccionados", + "save": "Guardar cohorte", + "saveAndClose": "Guardar y cerrar", + "saveAndSwitch": "Guardar y cambiar", + "textField": "Nuevo nombre de cohorte", + "title": "Guardar nueva cohorte" }, "Dashboard": { "allData": "Todos los datos", - "columnOne": "Image", + "columnOne": "Imagen", "columnTwo": "Índice", "columnThree": "Y verdadero", "columnFour": "Eje Y previsto", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Correcto", + "columnFourOD": "Incorrecto", "columnFive": "Otros metadatos", - "chooseObject": "Choose a detected object", - "examples": "examples", + "chooseObject": "Elegir un objeto detectado", + "examples": "Ejemplos", "filter": "Filtro", - "indexLabel": "Image ", - "labelTypeDropdown": "Select label type", - "labelVisibilityDropdown": "Select labels to display", - "legendFailure": "failure", - "legendSuccess": "success", - "loading": "Computing explanation for index", - "multiselect": "Multiselect", - "notdefined": "object scenario not defined", - "objectSelect": "Object Selection", + "indexLabel": "Imagen ", + "labelTypeDropdown": "Seleccionar tipo de etiqueta", + "labelVisibilityDropdown": "Seleccionar etiquetas para mostrar", + "legendFailure": "error", + "legendSuccess": "correcto", + "loading": "Explicación de computación para el índice", + "multiselect": "Selección múltiple", + "notdefined": "escenario de objeto no definido", + "objectSelect": "Selección de objetos", "pageSize": "Tamaño de página: ", - "panelTitle": "Selected instance", - "panelExplanation": "Explanation", - "panelInformation": "Information", - "predictedLabel": "Predicted label", - "predictedY": "Predicted: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", - "prefix": "Object: ", - "rows": "Rows: ", + "panelTitle": "Instancia seleccionada", + "panelExplanation": "Explicación", + "panelInformation": "Información", + "predictedLabel": "Etiqueta prevista", + "predictedY": "Previsto: ", + "correctDetections": "Detecciones correctas: ", + "incorrectDetections": "Detecciones incorrectas: ", + "prefix": "Objeto: ", + "rows": "Filas: ", "search": "Buscar", - "selectAll": "Select all", + "selectAll": "Seleccionar todo", "settings": "Configuración", - "showAll": "Show all", + "showAll": "Mostrar todo", "tabOptionFirst": "Vista del explorador de imágenes", "tabOptionSecond": "Vista de tabla", - "tabOptionThird": "Class view", + "tabOptionThird": "Vista de clases", "thumbnailSize": "Tamaño de la miniatura", "titleBarError": "Instancias de error", "titleBarSuccess": "Instancias correctas", - "trueY": "Ground truth: " + "trueY": "Verdad del suelo: " } }, "ModelAssessment": { @@ -1239,15 +1241,15 @@ "CalloutContent": "Agregar algunos componentes (vista de árbol de error, mapa térmico de error) le permitirá filtrar los datos de la cohorte global que puede ver en los componentes a continuación.", "CalloutTitle": "Agregar componente", "TabAddedMessage": { - "DataAnalysis": "Data analysis component added", - "FeatureImportances": "Feature importances component added", - "ErrorAnalysis": "Error analysis component added", - "Fairness": "Fairness component added", - "ModelOverview": "Model overview component added", - "CausalAnalysis": "Causal analysis component added", - "Counterfactuals": "Counterfactuals component added", - "Vision": "Vision data explorer component added", - "Forecasting": "Forecasting what-if component added" + "DataAnalysis": "Componente de análisis de datos agregado", + "FeatureImportances": "Componente de importancias de características agregado", + "ErrorAnalysis": "Componente de análisis de errores agregado", + "Fairness": "Componente de equidad agregado", + "ModelOverview": "Componente de información general del modelo agregado", + "CausalAnalysis": "Componente de análisis causal agregado", + "Counterfactuals": "Componente de contrafactuales agregado", + "Vision": "Se ha agregado el componente explorador de datos de Vision", + "Forecasting": "Componente what-if de previsión agregado" } }, "CausalAnalysis": { @@ -1275,7 +1277,7 @@ }, "CohortInformation": { "ShiftCohort": "Cambiar cohorte", - "SwitchTimeSeries": "Switch time series", + "SwitchTimeSeries": "Cambiar serie temporal", "NewCohort": "Nueva cohorte", "DataPoints": "Número de puntos de datos", "DefaultCohort": " (predeterminada)", @@ -1287,7 +1289,7 @@ "CohortSettingsTitle": "Configuración de cohorte" }, "ComponentNames": { - "ChartView": "Chart view", + "ChartView": "Vista de gráfico", "CausalAnalysis": "Análisis causal", "Counterfactuals": "Elementos contrafactuales", "DataAnalysis": "Análisis de datos", @@ -1296,10 +1298,10 @@ "ErrorAnalysis": "Análisis de errores", "Fairness": "Equidad", "FeatureImportances": "Importancia de la característica", - "Forecasting": "Forecasting", + "Forecasting": "Previsión", "ModelOverview": "Información general del modelo", - "TableView": "Table view", - "VisionTab": "Vision data explorer" + "TableView": "Vista de tabla", + "VisionTab": "Explorador de datos de Vision" }, "DashboardSettings": { "Content": "Esta lista muestra el diseño del panel. Puede filtrar los datos mediante el componente de análisis de errores, para verlos en los componentes siguientes.", @@ -1458,16 +1460,16 @@ "GlobalExplanation": "Importancia de la característica Agregar", "IncorrectPredictions": "Predicciones incorrectas", "InfoTitle": "Additional information on feature importance values", - "IndividualFeatureTabular": "Select a datapoint by clicking on a datapoint (up to 5 datapoints) in the table to view their local feature importance values (local explanation) and individual conditional expectation (ICE) plots.", + "IndividualFeatureTabular": "Seleccione un punto de datos haciendo clic en un punto de datos (hasta 5 puntos de datos) de la tabla para ver los valores de importancia de las características locales (explicación local) y el trazado de expectativa condicional individual (ICE) a continuación.", "IndividualFeatureText": "Select a datapoint by clicking on a datapoint in the table to view the local feature importance values (local explanation) from SHAP's text explainer.", "LocalExplanation": "Importancia de la característica Individual", "SelectionCounter": "{0}/{1} puntos de datos seleccionados", "SelectionLimit": "En este momento se pueden seleccionar hasta 5 puntos de datos.", - "RowCheckboxAriaLabel": "Row checkbox", - "SelectionColumnAriaLabel": "Toggle selection" + "RowCheckboxAriaLabel": "Casilla de verificación de fila", + "SelectionColumnAriaLabel": "Alternar selección" }, "IndividualFeatureImportanceView": { - "SmallInstanceSelection": "Instance selection" + "SmallInstanceSelection": "Selección de instancia" }, "MainMenu": { "DashboardSettings": "Configuración del panel", @@ -1483,44 +1485,44 @@ "ModelOverview": { "metrics": { "accuracy": { - "name": "Accuracy score", + "name": "Puntuación de precisión", "description": "Fracción de puntos de datos clasificada correctamente." }, "exactMatchRatio": { - "name": "Exact match ratio", - "description": "The ratio of instances classified correctly for every label." + "name": "Relación de coincidencia exacta", + "description": "La proporción de instancias clasificadas correctamente para cada etiqueta." }, "meteorScore": { - "name": "Meteor Score", - "description": "METEOR Score is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision in question answering task." + "name": "Puntuación de meteorología", + "description": "La puntuación meteoro se calcula en función de la media de precisión y recuperación, con la recuperación ponderada más que la precisión en la tarea de respuesta a preguntas." }, "bleuScore": { - "name": "Bleu Score", - "description": "Bleu Score measures the ratio of words (and/or n-grams) in the machine generated text that appeared in the reference text in question answering task." + "name": "Puntuación de puntuación de puntuación", + "description": "Puntuación de color azul mide la proporción de palabras (y/o n-gramas) en el texto generado por la máquina que aparecía en el texto de referencia en la tarea de respuesta a preguntas." }, "bertScore": { - "name": "Bert Score", - "description": "BERTScore focuses on computing semantic similarity between tokens of reference and machine generated text in question answering task." + "name": "Puntuación bert", + "description": "BERTScore se centra en calcular la similitud semántica entre los tokens de referencia y el texto generado por la máquina en la tarea de respuesta a preguntas." }, "rougeScore": { - "name": "Rouge Score", - "description": "Rouge Score measures the ratio of words (and/or n-grams) in the reference text that appeared in the machine generated text in question answering task." + "name": "Puntuación de Ósin", + "description": "Puntuación de Score mide la proporción de palabras (y/o n-gramas) en el texto de referencia que aparecía en el texto generado por el equipo en la tarea de respuesta a preguntas." }, "hammingScore": { - "name": "Hamming score", - "description": "The average ratio of labels classified correctly among those classified as 1 in multilabel task." + "name": "Puntuación de hamming", + "description": "La proporción media de etiquetas clasificadas correctamente entre las clasificadas como 1 en la tarea de varias etiquetas." }, "f1Score": { "name": "Puntuación F1", "description": "La puntuación F1 es la media armónica de precisión y coincidencia." }, "f1ScoreMacro": { - "name": "Macro F1 score", - "description": "Macro F1 score is the harmonic mean of precision and recall for each class, with each class weighted equally." + "name": "Puntuación F1 macro", + "description": "La puntuación F1 de macro es la media de precisión y recuperación para cada clase, con cada clase ponderada por igual." }, "f1ScoreMicro": { - "name": "Micro F1 score", - "description": "Micro F1 score is the harmonic mean of precision and recall for each class, with each class weighted according to how many instances it contains." + "name": "Puntuación F1 micro", + "description": "La puntuación micro F1 es la media de precisión y recuperación para cada clase, con cada clase ponderada según el número de instancias que contiene." }, "meanAbsoluteError": { "name": "Error absoluto medio", @@ -1535,24 +1537,24 @@ "description": "Fracción de puntos de datos clasificada correctamente entre los clasificados como 1." }, "precisionMacro": { - "name": "Macro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted equally." + "name": "Puntuación de precisión de macro", + "description": "Fracción de puntos de datos clasificados correctamente entre los clasificados como 1 para cada clase con cada clase ponderada por igual." }, "precisionMicro": { - "name": "Micro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted according to how many instances it contains." + "name": "Puntuación de micro precisión", + "description": "Fracción de puntos de datos clasificados correctamente entre los clasificados como 1 para cada clase con cada clase ponderada según el número de instancias que contiene." }, "recall": { "name": "Puntuación de coincidencia", "description": "Fracción de puntos de datos clasificada correctamente entre aquellos cuya etiqueta verdadera es 1. Nombres alternativos: índice de verdaderos positivos, confidencialidad." }, "recallMacro": { - "name": "Macro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted equally." + "name": "Puntuación de recuperación de macros", + "description": "Fracción de puntos de datos clasificados correctamente entre aquellos cuya etiqueta verdadera es 1 para cada clase con cada clase ponderada por igual." }, "recallMicro": { - "name": "Micro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted according to how many instances it contains." + "name": "Puntuación de micro recuperación", + "description": "Fracción de puntos de datos clasificados correctamente entre aquellos cuya etiqueta verdadera es 1 para cada clase con cada clase ponderada según el número de instancias que contiene." }, "falsePositiveRate": { "name": "Tasa de falsos positivos", @@ -1571,32 +1573,32 @@ "description": "Promedio de todas las predicciones." }, "meanAveragePrecision": { - "name": "Mean Average Precision score", - "description": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall." + "name": "Puntuación media de precisión media", + "description": "La precisión media para los modelos de detección de objetos es el promedio de AP (precisión media) en todas las clases. Esto evalúa la solidez del modelo de detección de objetos y encapsula el equilibrio entre precisión y recuperación." }, "averagePrecision": { - "name": "Average Precision score", - "description": "Average precision for object detection models is calculated for a selected class." + "name": "Puntuación de precisión media", + "description": "La precisión media de los modelos de detección de objetos se calcula para una clase seleccionada." }, "averageRecall": { - "name": "Average Recall score", - "description": "Average recall for object detection models is calculated for a selected class." + "name": "Puntuación promedio de recuperación", + "description": "La recuperación media de los modelos de detección de objetos se calcula para una clase seleccionada." }, "fairnessMetricDifference": "Diferencia", "fairnessMetricRatio": "Relación" }, "metricsDropdown": "Métricas", - "metricsTypeDropdown": "Aggregate method", + "metricsTypeDropdown": "Método de agregado", "metricTypes": { "macro": "Macro", "micro": "Micro" }, - "classSelectionDropdown": "Select class(es)", + "classSelectionDropdown": "Seleccionar clase", "iouThresholdDropdown": { - "name": "IoU Threshold", - "description": "Intersection over Union quantifies the degree of overlap between the prediction and ground truth bounding box of a detected object in an image. For example, setting an IoU threshold of 70% means that a prediction with greater than 70% overlap with ground truth is True, thus influencing the definition of prediction correctness and calculation of other performance metrics.", + "name": "Umbral IOU:", + "description": "La intersección sobre Union cuantifica el grado de superposición entre la predicción y el rectángulo delimitador de la verdad terrestre de un objeto detectado en una imagen. Por ejemplo, establecer un umbral de IoU del 70 % significa que una predicción con una superposición superior al 70 % con la veracidad del terreno es Verdadera, lo que influye en la definición de corrección de predicción y el cálculo de otras métricas de rendimiento.", "iconId": "iouThresholdIconId", - "title": "Learn about the IoU threshold" + "title": "Más información sobre el umbral de IoU" }, "notAvailable": "N/D", "countColumnHeader": "Tamaño de muestra", @@ -1608,14 +1610,14 @@ "featuresDropdown": "Características", "metricChartDropdownSelectionHeader": "Métrica", "probabilityForClassSelectionHeader": "Probabilidad de clase", - "targetSelectionHeader": "Target", + "targetSelectionHeader": "Destino", "metricSelectionDropdownPlaceholder": "Seleccione las métricas para comparar las cohortes.", - "classSelectionDropdownPlaceholder": "Select class name for class-based analysis.", + "classSelectionDropdownPlaceholder": "Seleccione el nombre de clase para el análisis basado en clases.", "featureSelectionDropdownPlaceholder": "Seleccione las características que se usarán para un análisis basado en características.", "probabilityDistributionPivotItem": "Distribución de probabilidad", - "regressionDistributionPivotItem": "Target distribution", + "regressionDistributionPivotItem": "Distribución de destino", "metricsVisualizationsPivotItem": "Visualizaciones de métricas", - "confusionMatrixPivotItem": "Confusion matrix", + "confusionMatrixPivotItem": "Matriz de confusión", "disaggregatedAnalysisFeatureSelectionPlaceholder": "Seleccione las características para generar el análisis basado en características.", "tableCountTooltip": "La cohorte {0} contiene {1} instancias.", "tableMetricTooltip": "El {0} del modelo en la cohorte {1} es {2}", @@ -1626,36 +1628,36 @@ "metricSelectionButton": "Elegir métrica", "cohortSelectionButton": "Elegir cohortes", "probabilityLabelSelectionButton": "Elegir etiqueta", - "regressionTargetSelectionButton": "Choose target", + "regressionTargetSelectionButton": "Elegir destino", "selectAllCohortsOption": "Seleccionar todo", "other": "Otros", "BoxPlot": { "outlierProbability": "probabilidad", "outlierLabel": "Valores atípicos", "boxPlotSeriesLabel": "Trazado de cuadro", - "lowerWhisker": "Lower whisker", - "upperWhisker": "Upper whisker", - "median": "Median", - "lowerQuartile": "Lower quartile", - "upperQuartile": "Upper quartile" + "lowerWhisker": "Valor mínimo", + "upperWhisker": "Bigote superior", + "median": "Mediana", + "lowerQuartile": "Cuartil inferior", + "upperQuartile": "Cuartil superior" }, "chartConfigApply": "Aplicar", "chartConfigCancel": "Cancelar", "chartConfigDatasetCohortSelectionPlaceholder": "Seleccionar cohortes de conjunto de datos", "chartConfigFeatureBasedCohortSelectionPlaceholder": "Seleccionar cohortes basadas en características", "confusionMatrix": { - "confusionMatrixCohortSelectionLabel": "Select dataset cohort", - "confusionMatrixClassSelectionLabel": "Select classes", - "confusionMatrixClassMinSelectionError": "Select at least {0} classes to visualize the confusion matrix.", - "confusionMatrixClassMaxSelectionError": "Select at most {0} classes to visualize the confusion matrix.", - "confusionMatrixClassSelectionDefaultPlaceholder": "Choose classes", - "confusionMatrixHeatmapTooltip": "{0} datapoints should be {1}, predicted to be {2}", - "confusionMatrixYAxisLabel": "True Class", - "confusionMatrixXAxisLabel": "Predicted Class", - "class": "Class" + "confusionMatrixCohortSelectionLabel": "Seleccionar cohortes de conjunto de datos", + "confusionMatrixClassSelectionLabel": "Seleccionar clases", + "confusionMatrixClassMinSelectionError": "Seleccione al menos {0} clases para visualizar la matriz de confusión.", + "confusionMatrixClassMaxSelectionError": "Seleccione como máximo {0} clases para visualizar la matriz de confusión.", + "confusionMatrixClassSelectionDefaultPlaceholder": "Elegir clases", + "confusionMatrixHeatmapTooltip": "{0} los puntos de datos deben ser {1}, se predice que {2}", + "confusionMatrixYAxisLabel": "Clase verdadera", + "confusionMatrixXAxisLabel": "Clase predicha", + "class": "Clase" }, "nA": "N/D", - "disaggregatedAnalysisBaseCohortDisclaimer": "The cohorts in the following feature-based analysis are based on the global cohort, {0}.", + "disaggregatedAnalysisBaseCohortDisclaimer": "Las cohortes del siguiente análisis basado en características se basan en la cohorte global, {0}.", "disaggregatedAnalysisBaseCohortWarning": "A diferencia de la cohorte {0}, {1} incluye filtros. Como consecuencia, solo captura un subconjunto de todo el conjunto de datos y es posible que la información no se generalice en el conjunto de datos completo.", "probabilitySplineChartToggleLabel": "Usar gráfico de spline", "countAxisLabel": "Recuento", @@ -1685,76 +1687,76 @@ "flyoutDescription": "Puede elegir ver cohortes de conjunto de datos o cohortes de características. Si las cohortes de características no están disponibles, primero debe seleccionar una o varias características en la vista de cohortes de características. Posteriormente, se generan cohortes de características y puede seleccionarlas aquí." }, "regressionTargetOptions": { - "predictedY": "Predicted Y", - "trueY": "True Y", + "predictedY": "Eje Y previsto", + "trueY": "Y verdadero", "error": "Error" }, "topLevelDescription": "Evalúe el rendimiento del modelo explorando la distribución de los valores de predicción y los valores de las métricas de rendimiento del modelo. Use la pestaña \"Cohortes del conjunto de datos\" para investigar el modelo examinando un análisis comparativo de su rendimiento en diferentes cohortes de conjuntos de datos creados previamente o recién creados. Use la pestaña \"Cohortes de características\" para investigar el modelo examinando un análisis comparativo de su rendimiento en subcohortes de características confidenciales o no confidenciales. (por ejemplo, rendimiento entre distintos sexos, niveles de ingresos).", - "infoTitle": "Additional information on model overview", + "infoTitle": "Información adicional sobre la información general del modelo", "visualDisplayToggleLabel": "Mostrar el mapa térmico", "featureBasedViewDescription": "Seleccione hasta dos características para ver el desglose del rendimiento del modelo entre cohortes basadas en características (si se selecciona una característica) o cohortes interseccionales (si se seleccionan dos características)." }, "TableViewTab": { - "Heading": "View the dataset in a table format for all features and rows." + "Heading": "Vea el conjunto de datos en un formato de tabla para todas las características y filas." } }, "Forecasting": { - "target": "Target", - "whatIfForecastingHeader": "What-if analysis", - "forecastHeader": "Forecast analysis", - "whatIfForecastingDescription": "What-if allows you to perturb features for your entire time series and observe how the model's forecast changes.", - "whatIfForecastingChooseTimeSeries": "To start, choose a time series from the options below.", - "forecastDescription": "Forecast analysis compares your model's forecast to the actual values of your time series. To enable what-if analysis, provide a dataset with features.", - "timeSeries": "Time series", - "selectTimeSeries": "Select a time series.", - "singleTimeSeries": "The dataset contains only a single time series '{0}' which has been selected by default.", - "trueY": "True Y", - "baselinePrediction": "Baseline prediction", - "forecastComparisonHeader": "Compare What-if Forecasts", - "forecastComparisonChartTitle": "Forecasts", - "forecastComparisonChartTimeAxisLabel": "Time", + "target": "Destino", + "whatIfForecastingHeader": "Análisis de hipótesis", + "forecastHeader": "Análisis de previsión", + "whatIfForecastingDescription": "What-if le permite perturbar las características de toda la serie temporal y observar cómo cambia la previsión del modelo.", + "whatIfForecastingChooseTimeSeries": "Para empezar, elija una serie temporal entre las opciones siguientes.", + "forecastDescription": "El análisis de previsión compara la previsión del modelo con los valores reales de la serie temporal. Para habilitar el análisis de hipótesis, proporcione un conjunto de datos con características.", + "timeSeries": "Serie temporal", + "selectTimeSeries": "Seleccionar una serie temporal", + "singleTimeSeries": "El conjunto de datos contiene solo una serie temporal \"{0}\" que se ha seleccionado de forma predeterminada.", + "trueY": "Y verdadero", + "baselinePrediction": "Predicción de línea base", + "forecastComparisonHeader": "Comparación de las previsiones de hipó previas", + "forecastComparisonChartTitle": "Previsiones", + "forecastComparisonChartTimeAxisLabel": "Hora", "Transformations": { - "multiply": "multiply", - "divide": "divide", - "add": "add", - "subtract": "subtract", - "change": "change to" + "multiply": "multiplicar", + "divide": "dividir", + "add": "sumar", + "subtract": "restar", + "change": "Cambiar a:" }, "TransformationCreation": { - "title": "Create what-if scenario", - "nameLabel": "What-if scenario name", - "featureInstructions": "Choose a feature to perturb.", - "operationInstructions": "Choose an operation to apply to the feature.", - "operationDropdownHeader": "Operation", - "featureDropdownHeader": "Feature", - "valueSpinButtonHeader": "Value", - "scenarioNamingInstructionsPlaceholder": "Enter a unique name", - "scenarioNamingInstructions": "Enter a name for your what-if scenario.", - "scenarioNamingCollisionMessage": "This name exists already. Please enter a unique name.", - "scenarioNamingLengthMessage": "The name must be between 1 and 50 characters. The actual length is {0}.", - "scenarioNamingInvalidCharactersMessage": "The name can only contain alphanumeric characters, whitespaces, dashes, or underscores, and needs to start with an alphanumeric character.", - "valueErrorMessage": "For operation {0} please select a value other than {1}.", - "invalidCombinationErrorMessage": "This is identical to an existing what-if scenario. Please change the feature, operation, or value.", - "addTransformationButton": "Add Transformation", - "divisionAndMultiplicationBy": "by" + "title": "Creación de un escenario de hipó previa", + "nameLabel": "Nombre del escenario what-if", + "featureInstructions": "Elija una característica para perturbar", + "operationInstructions": "Elija una operación para aplicarla a la característica.", + "operationDropdownHeader": "Operación", + "featureDropdownHeader": "Característica", + "valueSpinButtonHeader": "Valor", + "scenarioNamingInstructionsPlaceholder": "Escriba un nombre único.", + "scenarioNamingInstructions": "Escriba un nombre para el escenario what-if.", + "scenarioNamingCollisionMessage": "Este nombre ya existe. Escriba un nombre único.", + "scenarioNamingLengthMessage": "El nombre debe tener entre 1 y 50 caracteres. La longitud real es {0}.", + "scenarioNamingInvalidCharactersMessage": "El nombre solo puede contener caracteres alfanuméricos, espacios en blanco, guiones o caracteres de subrayado, y debe comenzar con un carácter alfanumérico.", + "valueErrorMessage": "Para la operación {0}, seleccione un valor distinto de {1}.", + "invalidCombinationErrorMessage": "Esto es idéntico a un escenario what-if existente. Cambie la característica, la operación o el valor.", + "addTransformationButton": "Agregar transformación", + "divisionAndMultiplicationBy": "por" }, "TransformationTable": { - "nameColumnHeader": "Name", - "methodColumnHeader": "Method", - "divisionAndMultiplicationBy": "by ", - "header": "What-if Forecasts ({0})" + "nameColumnHeader": "Nombre", + "methodColumnHeader": "Método", + "divisionAndMultiplicationBy": "por ", + "header": "Previsiones what-if ({0})" }, "TimeSeries": { - "apply": "Apply", - "cancel": "Cancel", - "cohortList": "Time series list", - "selectCohort": "Select a time series", - "shiftCohort": "Switch time series", - "shiftCohortDescription": "Select a time series from the time series list. Apply the time series to the dashboard." + "apply": "Aplicar", + "cancel": "Cancelar", + "cohortList": "Lista de series temporales", + "selectCohort": "Seleccionar una serie temporal", + "shiftCohort": "Cambiar serie temporal", + "shiftCohortDescription": "Seleccione una serie temporal de la lista de series temporales. Aplique la serie temporal al panel." }, "TimeSeriesSettings": { - "CohortSettingsDescription": "Time series are pre-defined based on time series identifying columns.", - "CohortSettingsTitle": "Time series settings" + "CohortSettingsDescription": "Las series temporales se definen previamente en función de la serie temporal que identifica las columnas.", + "CohortSettingsTitle": "Configuración de series temporales" } } } \ No newline at end of file diff --git a/libs/localization/src/lib/en.fr.json b/libs/localization/src/lib/en.fr.json index ef4ebbd039..3a1cac7de2 100644 --- a/libs/localization/src/lib/en.fr.json +++ b/libs/localization/src/lib/en.fr.json @@ -906,6 +906,8 @@ "index": "Index", "output": "Résultat", "predictedY": "Valeur Y prédite", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Probabilité : {0}", "trueY": "Y réel", "xValue": "Valeur X :", @@ -1147,7 +1149,7 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Tableau de bord d’interprétation", - "importantWords": "Show most important words", + "importantWords": "Afficher les mots les plus importants", "topFeatureList": "Analyse de la liste des principales fonctionnalités", "allButton": "TOUTES LES FONCTIONNALITÉS", "negButton": "FONCTIONNALITÉS NÉGATIVES", @@ -1162,7 +1164,7 @@ "trueAnswer": "Réponse vraie : ", "inputs": "Entrées", "outputs": "Sorties", - "sliderAriaLabel": "Slider for most important words" + "sliderAriaLabel": "Curseur pour les mots les plus importants" }, "Legend": { "featureLegend": "LÉGENDE DE FONCTIONNALITÉ DE TEXTE", @@ -1216,8 +1218,8 @@ "panelInformation": "Informations", "predictedLabel": "Libellé prédit", "predictedY": "Prédit : ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", + "correctDetections": "Détections correctes : ", + "incorrectDetections": "Détections incorrectes : ", "prefix": "Objet : ", "rows": "Lignes : ", "search": "Rechercher", diff --git a/libs/localization/src/lib/en.hu.json b/libs/localization/src/lib/en.hu.json index e26e509de3..ad969969e0 100644 --- a/libs/localization/src/lib/en.hu.json +++ b/libs/localization/src/lib/en.hu.json @@ -906,6 +906,8 @@ "index": "Index", "output": "Kimenet", "predictedY": "Előrejelzett Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Valószínűség: {0}", "trueY": "Valós Y", "xValue": "X-érték:", @@ -1147,7 +1149,7 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Értelmezhetőségi irányítópult", - "importantWords": "Show most important words", + "importantWords": "A legfontosabb szavak megjelenítése", "topFeatureList": "Kiemelt jellemzők listájának elemzése", "allButton": "MINDEN JELLEMZŐ", "negButton": "NEGATÍV JELLEMZŐK", @@ -1162,7 +1164,7 @@ "trueAnswer": "Igaz válasz: ", "inputs": "Bemenetek", "outputs": "Kimenetek", - "sliderAriaLabel": "Slider for most important words" + "sliderAriaLabel": "Csúszka a legfontosabb szavakhoz" }, "Legend": { "featureLegend": "SZÖVEGJELLEMZŐ JELMAGYARÁZATA", @@ -1195,8 +1197,8 @@ "columnTwo": "Index", "columnThree": "Valós Y", "columnFour": "Előrejelzett Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Helyes", + "columnFourOD": "Helytelen", "columnFive": "Egyéb metaadatok", "chooseObject": "Észlelt objektum kiválasztása", "examples": "példák", @@ -1216,8 +1218,8 @@ "panelInformation": "Információ", "predictedLabel": "Előrejelzett címke", "predictedY": "Előre jelzett: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", + "correctDetections": "Helyes észlelések: ", + "incorrectDetections": "Helytelen észlelések: ", "prefix": "Objektum: ", "rows": "Sorok: ", "search": "Keresés", diff --git a/libs/localization/src/lib/en.it.json b/libs/localization/src/lib/en.it.json index 7404e8c717..5af6949928 100644 --- a/libs/localization/src/lib/en.it.json +++ b/libs/localization/src/lib/en.it.json @@ -3,26 +3,26 @@ "close": "Chiudi", "tooltipButton": "Pulsante descrizione comando", "identityFeature": "Funzionalità di identità", - "infoTitle": "Additional information", - "spinButton": "Spin", - "editButton": "Edit", - "decreaseValue": "Decrease value", - "increaseValue": "Increase value", - "decreaseValueByOne": "Decrease value by 1", - "increaseValueByOne": "Increase value by 1", - "loading": "Loading..." + "infoTitle": "Informazioni aggiuntive", + "spinButton": "Casella di selezione", + "editButton": "Modifica", + "decreaseValue": "Riduci valore", + "increaseValue": "Aumenta valore", + "decreaseValueByOne": "Riduci valore di 1", + "increaseValueByOne": "Aumenta valore di 1", + "loading": "Caricamento…" }, "ChartContextMenu": { - "hideData": "Hide data table", - "viewData": "View data table", - "viewInFullScreen": "View in full screen", - "printChart": "Print chart", - "downloadCSV": "Download CSV", - "downloadPNG": "Download PNG image", - "downloadJPEG": "Download JPEG image", - "downloadPDF": "Download PDF document", - "downloadSVG": "Download SVG vector image", - "downloadXLS": "Download XLS" + "hideData": "Nascondere tabella dati", + "viewData": "Visualizzare tabella dati", + "viewInFullScreen": "Visualizzare a schermo intero", + "printChart": "Stampa grafico", + "downloadCSV": "Scaricare CSV", + "downloadPNG": "Scaricare immagine PNG", + "downloadJPEG": "Scaricare immagine JPEG", + "downloadPDF": "Scaricare documento PDF", + "downloadSVG": "Scaricare immagine vettoriale SVG", + "downloadXLS": "Scaricare XLS" }, "CausalAnalysis": { "AggregateView": { @@ -39,7 +39,7 @@ "description": "L'analisi causale risponde alle domande What-if, sul modo in cui i risultati reali potrebbero cambiare in funzione di diverse scelte di criteri, come ad esempio una strategia tariffaria diversa per un prodotto o un trattamento alternativo per un paziente. A differenza delle stime dei modelli che identificano modelli di correlazione importanti, questi strumenti consentono di identificare le caratteristiche causali più importanti che influiscono direttamente sul risultato di interesse. Questi modelli identificano l'effetto causale di una caratteristica (generalmente definita “trattamento”), mantenendo costanti le altre caratteristiche confondenti. Per ottenere risultati ottimali, assicurarsi che il set di dati completo contenga tutte le caratteristiche disponibili che possono essere correlate al risultato come confondenti.", "directAggregate": "Effetto causale aggregato diretto di ogni trattamento con intervallo di confidenza pari al 95%", "here": "qui", - "infoTitle": "Additional information on aggregated causal effects", + "infoTitle": "Informazioni aggiuntive sugli effetti causali aggregati", "lasso": "Un lazo (o regressione logistica se y è binario) era in grado di prevedere y da X[-i], e un lazo (o regressione logistica se X[i] è categorico) era in grado di prevedere X[i] da Χ [-i]. L'effetto causale può essere considerato come la correlazione media dei residui/variazione imprevista delle due attività di previsione. Altre informazioni su Double Machine Learning", "unconfounding": "Informazioni sulle funzionalità confondenti" }, @@ -51,7 +51,7 @@ "description": "Gli effetti causali individuali possono informare gli interventi personalizzati, come ad esempio una promozione mirata ai clienti o un piano di trattamento personalizzato. In che modo un individuo con uno specifico set di caratteristiche risponde a una modifica in una funzionalità causale o trattamento? Lo strumento causale What-If, calcola le modifiche marginali nei risultati reali per un particolare individuo se si modifica il livello di un trattamento. Questa analisi consente di capire in che modo i risultati reali possono cambiare in funzione di scelte diverse di criteri, come ad esempio una strategia tariffaria diversa per un prodotto o un trattamento alternativo per un paziente. Specificare il trattamento di interesse e osservare il modo in cui può cambiare il risultato reale.", "directIndividual": "Effetto causale individuale diretto di ogni trattamento con intervallo di confidenza pari al 95%", "index": "Indice di punto dati", - "infoTitle": "Additional information on individual causal what-if", + "infoTitle": "Informazioni aggiuntive sui singole simulazioni causali", "missingParameters": "Questa scheda richiede che sia fornito un set di dati di valutazione.", "newOutcome": "Nuovo risultato", "selectTreatment": "Selezionare un trattamento", @@ -85,7 +85,7 @@ "averageGainBinary": "Profitti medi dell'impostazione del {0} di trattamento al suo valore di base {1}.", "averageGainContinuous": "Guadagni medi di criteri alternativi rispetto al trattamento '{0}'.", "header": "Questi strumenti consentono di creare criteri per gli interventi futuri. È possibile identificare quali parti del campione sperimentano risposte maggiori alle modifiche nelle caratteristiche causali o trattamenti e costruire regole per definire quali popolazioni future dovranno essere destinate a particolari interventi.", - "infoTitle": "Additional information on treatment policy", + "infoTitle": "Ulteriori informazioni sui criteri di trattamento", "nSample": "n = {0}", "noData": "Nessun dato" } @@ -116,8 +116,8 @@ "cancel": "Annulla", "title": "Cambia coorte", "subText": "Selezionare una coorte dall'elenco di coorte. Applicare la coorte al dashboard.", - "selectCohort": "Select a cohort", - "cohortList": "Cohort list" + "selectCohort": "Selezionare una coorte", + "cohortList": "Elenco coorti" }, "PreBuiltCohort": { "featureNameNotFound": "Il nome della funzionalità non è stato trovato nel set di dati", @@ -148,13 +148,13 @@ "predictedClass": "Classe stimata", "predictedValue": "Valore stimato" }, - "Size": "Size", - "loading": "Loading...", + "Size": "Dimensioni", + "loading": "Caricamento...", "counterfactualEx": "Esempio controfattuale {0}", "counterfactualName": "Nome controfattuale di simulazione", "createWhatIfCounterfactual": "Creare controfattuale di simulazione", "createCounterfactual": "Controfattuale", - "revertToBubbleChart": "View bubble chart", + "revertToBubbleChart": "Visualizza grafico a bolle", "createOwn": "Crea il tuo controfattuale:", "currentClass": "Classe corrente", "currentRange": "Intervallo corrente", @@ -167,9 +167,9 @@ "listDescription": "Questa lista mostra quali punti dati nel campione di dati corrente hanno la maggiore risposta causale al trattamento selezionato, sulla base di tutte le caratteristiche incluse nel modello causale stimato. Le cinque colonne a sinistra segnalano se il trattamento è consigliato per l'osservazione, il trattamento corrente, l'effetto stimato del trattamento (l’effetto dell'applicazione di un trattamento da una linea di base di nessun trattamento per i trattamenti binari o aumentando/diminuendo la caratteristica di trattamento del 10% delle dimensioni tipiche del trattamento nel campione: [Dynamic: segnalare la modifica numerica del trattamento usato] ) e gli intervalli di confidenza inferiori e superiori (CI) per questo effetto. Le colonne rimanenti mostrano lo stato del trattamento corrente e altre caratteristiche di ciascuna osservazione.", "localImportanceDescription": "Le caratteristica di primo livello nella riga {0} da perturbare ottenere la previsione del modello desiderata. In base all'analisi What-if per la previsione: {1}", "localImportanceSelectData": "Selezionare un punto dati per visualizzare il grafico delle priorità locali", - "largeLocalImportanceSelectData": "Select a bubble, followed by a data point to view local importance chart", - "localImportanceFetchError": "There was an error while fetching the local importance data. Error details: {0} Please check the data used.", - "BubbleChartFetchError": "There was an error while fetching the data. Error details: {0} Please check the data used.", + "largeLocalImportanceSelectData": "Selezionare una bolla, seguita da un punto dati per visualizzare il grafico delle priorità locali", + "localImportanceFetchError": "Si è verificato un errore durante il recupero dei dati sull'importanza locale. Dettagli errore: {0} Controllare i dati usati.", + "BubbleChartFetchError": "Si è verificato un errore durante il recupero dei dati. Dettagli errore: {0} Controllare i dati usati.", "noData": "Dati non disponibili", "noFeatures": "Nessuna funzionalità disponibile", "panelDescription": "Esplora i controfattuali e creane uno personalizzato. Ricerca nelle funzionalità per visualizzare i valori suggeriti da un set eterogeneo di esempi controfattuali. Imposta i valori delle funzionalità controfattuali suggeriti facendo “clic su Imposta testo valore” sotto ogni nome controfattuale. Assegna un nome al contatore e salvarlo.", @@ -223,13 +223,13 @@ "subText": "Informazioni sulla coorte selezionata. Modificarne il nome. Eliminare la coorte." }, "FeatureList": { - "featureList": "Feature List", + "featureList": "Elenco funzionalità", "apply": "Applica", "features": "Funzionalità", "importances": "Priorità", "treeMapDescription": "Per ripetere il training della mappa ad albero, selezionare e salvare le caratteristiche seguenti. Le priorità delle caratteristiche sono state calcolate usando informazioni comuni con l’errore nelle etichette vere. Usarlo come riferimento per il traning della mappa ad albero.", "staticTreeMapDescription": "Visualizza le funzionalità usate per eseguire il training della mappa ad albero. Le priorità delle funzionalità sono state calcolate usando informazioni reciproche con l'errore sulle etichette reali.", - "searchResultMessage": "Results displayed out of {resultLength} for {searchValue}" + "searchResultMessage": "Risultati visualizzati su {resultLength} per {searchValue}" }, "TreeViewParameters": { "maximumDepth": "Profondità massima", @@ -295,7 +295,7 @@ "disabledWarning": "La mappa termica di errore è disabilitata a meno che la coorte globale non venga impostata per rappresentare \"Tutti i dati\" a causa della generazione della mappa termica per il set di dati completo. Tornare al set di dati completo per visualizzare la mappa termica degli errori." }, "MatrixSummary": { - "heatMapInfoTitle": "Additional information on heat map", + "heatMapInfoTitle": "Altre informazioni sulla mappa termica", "heatMapDescription": "Con la mappa termica è possibile concentrarsi su specifici filtri di caratteristiche intersezionali e calcolare le frequenze di errore disaggregate. Iniziare con due funzionalità del set di dati da confrontare.", "heatMapStaticDescription": "Con la mappa termica è possibile concentrarsi su specifici filtri di funzionalità intersezionali e sulla frequenza di errori disaggregati di calcolo. È necessario selezionare fino a due funzionalità per creare una mappa termica tramite SDK prima di visualizzare il dashboard." }, @@ -311,108 +311,108 @@ }, "Metrics": { "AccuracyScore": { - "Name": "Accuracy score", - "Info": "The accuracy score represents the ratio of correct to total instances in the data.", - "Short": "Accuracy", - "Title": "Additional information on accuracy score" + "Name": "Punteggio di accuratezza", + "Info": "Il punteggio di accuratezza rappresenta il rapporto tra le istanze corrette e le istanze totali nei dati.", + "Short": "Accuratezza", + "Title": "Informazioni aggiuntive sul punteggio di accuratezza" }, "ErrorRate": { - "Name": "Error rate", - "Info": "The error rate represents the percentage of instances in the node for which the system has failed.", - "Short": "Error rate", - "Title": "Additional information on error rate" + "Name": "Percentuale di errore", + "Info": "La percentuale di errore rappresenta la percentuale di istanze nel nodo per cui il sistema non è riuscito.", + "Short": "Percentuale di errore", + "Title": "Altre informazioni sulla percentuale di errori" }, "F1Score": { - "Name": "F1 score", - "Info": "The F1 score is the harmonic mean of the precision and recall metrics.", - "Short": "F1 score", - "Title": "Additional information on F1 score" + "Name": "Punteggio F1", + "Info": "Il punteggio F1 è la media armonica delle metriche di precisione e richiamo.", + "Short": "Punteggio F1", + "Title": "Altre informazioni sul punteggio F1" }, "MeanAbsoluteError": { - "Name": "Mean absolute error", - "Info": "The mean absolute error is the average of the sum of the errors.", - "Short": "Mean abs. error", - "Title": "Additional information on mean absolute error" + "Name": "Errore assoluto medio", + "Info": "L'errore assoluto medio è la media della somma degli errori.", + "Short": "Errore medio abs.", + "Title": "Informazioni aggiuntive sull'errore assoluto medio" }, "MeanSquaredError": { - "Name": "Mean squared error", - "Info": "The mean squared error is the average of the squares of the errors.", - "Short": "Mean sq. error", - "Title": "Additional information on mean squared error" + "Name": "Errore quadratico medio", + "Info": "L'errore quadratico medio è la media dei quadrati degli errori.", + "Short": "Errore quadratico medio", + "Title": "Informazioni aggiuntive sull'errore quadratico medio" }, "Precision": { - "Name": "Precision score", - "Info": "The precision is the ratio of true positives over all predicted positives.", - "Short": "Precision", - "Title": "Additional information on precision" + "Name": "Punteggio di precisione", + "Info": "La precisione è il rapporto tra veri positivi rispetto a tutti i positivi stimati.", + "Short": "Precisione", + "Title": "Informazioni aggiuntive sulla precisione" }, "Recall": { - "Name": "Recall score", - "Info": "The recall is the ratio of true positives over all actual positives.", - "Short": "Recall", - "Title": "Additional information on recall" + "Name": "Punteggio di richiamo", + "Info": "Il richiamo è il rapporto tra i veri positivi rispetto a tutti i positivi effettivi.", + "Short": "Richiama", + "Title": "Altre informazioni sul richiamo" }, "MacroPrecision": { - "Name": "Macro averaged precision score", - "Info": "The macro averaged precision is the ratio of true positives over all predicted positives computed independently per class and averaged.", - "Short": "Macro precision", - "Title": "Additional information on macro averaged precision" + "Name": "Punteggio di precisione media macro", + "Info": "La precisione media è il rapporto tra veri positivi su tutti i positivi effettivi stimati calcolati in modo indipendente per classe e mediati.", + "Short": "Precisione macro", + "Title": "Informazioni aggiuntive sulla precisione media delle macro" }, "MicroPrecision": { - "Name": "Micro averaged precision score", - "Info": "The micro averaged precision is the ratio of true positives over all predicted positives aggregated for all classes.", - "Short": "Micro precision", - "Title": "Additional information on micro averaged precision" + "Name": "Punteggio di precisione media micro", + "Info": "Il richiamo micromedio è il rapporto tra veri positivi rispetto a tutti i positivi effettivi aggregati per tutte le classi.", + "Short": "Micro precisione", + "Title": "Informazioni aggiuntive sulla precisione media delle micro" }, "MacroRecall": { - "Name": "Macro averaged recall score", - "Info": "The macro averaged recall is the ratio of true positives over all actual positives computed independently per class and averaged.", - "Short": "Macro recall", - "Title": "Additional information on macro averaged recall" + "Name": "Punteggio di richiamo medio macro", + "Info": "Il richiamo medio macro è il rapporto tra veri positivi su tutti i positivi effettivi calcolati in modo indipendente per classe e mediati.", + "Short": "Richiamo macro", + "Title": "Informazioni aggiuntive sul richiamo medio delle macro" }, "MicroRecall": { - "Name": "Micro averaged recall score", - "Info": "The micro averaged recall is the ratio of true positives over all actual positives aggregated for all classes.", - "Short": "Micro recall", - "Title": "Additional information on micro averaged recall" + "Name": "Punteggio di richiamo medio micro", + "Info": "Il richiamo micromedio è il rapporto tra veri positivi rispetto a tutti i positivi effettivi aggregati per tutte le classi.", + "Short": "Richiamo micro", + "Title": "Informazioni aggiuntive sul richiamo medio delle micro" }, "MacroF1Score": { - "Name": "Macro averaged F1 score", - "Info": "The macro averaged F1 score is the harmonic mean of the macro averaged precision and recall metrics.", - "Short": "Macro F1 score", - "Title": "Additional information on macro averaged F1 score" + "Name": "Punteggio medio delle macro F1", + "Info": "Il punteggio F1 medio della macro è la media armonica delle metriche di precisione media e richiamo della macro.", + "Short": "Punteggio F1 macro", + "Title": "Informazioni aggiuntive sul punteggio medio delle macro F1" }, "MicroF1Score": { - "Name": "Micro averaged F1 score", - "Info": "The micro averaged F1 score is the harmonic mean of the micro averaged precision and recall metrics.", - "Short": "Micro F1 score", - "Title": "Additional information on micro averaged F1 score" + "Name": "Punteggio medio delle micro F1", + "Info": "Il punteggio F1 medio micro è la media armonica delle metriche micro medie di precisione e richiamo.", + "Short": "Punteggio Micro F1", + "Title": "Informazioni aggiuntive sul punteggio medio delle micro F1" }, "MeanAveragePrecision": { - "Name": "Mean average precision score", - "Info": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall.", - "Short": "Mean avg precision", - "Title": "Additional information on mean average precision score" + "Name": "Punteggio precisione media", + "Info": "La precisione media per i modelli di rilevamento oggetti è la media di AP (precisione media) in tutte le classi. Ciò valuta l'affidabilità del modello di rilevamento oggetti e incapsula il compromesso tra precisione e richiamo.", + "Short": "Media precisione media", + "Title": "Informazioni aggiuntive sul punteggio medio di precisione media" }, "AveragePrecision": { - "Name": "Average precision score", - "Info": "Average precision for object detection models is calculated for a selected class.", - "Short": "Avg precision", - "Title": "Additional information on average precision score" + "Name": "Punteggio di precisione media", + "Info": "La precisione media per i modelli di rilevamento oggetti viene calcolata per una classe selezionata.", + "Short": "Precisione media", + "Title": "Informazioni aggiuntive sul punteggio di precisione media" }, "AverageRecall": { - "Name": "Average recall score", - "Info": "Average recall for object detection models is calculated for a selected class.", - "Short": "Avg recall", - "Title": "Additional information on average recall score" + "Name": "Punteggio di richiamo medio", + "Info": "La precisione media per i modelli di rilevamento oggetti viene calcolata per una classe selezionata.", + "Short": "Richiamo medio", + "Title": "Informazioni aggiuntive sul punteggio di richiamo medio" }, "metricName": "Nome della metrica", "metricValue": "Valore della metrica" }, "MetricSelector": { "selectorLabel": "Selezionare la metrica", - "feature1SelectorLabel": "Rows: Feature 1", - "feature2SelectorLabel": "Columns: Feature 2" + "feature1SelectorLabel": "Righe: funzionalità 1", + "feature2SelectorLabel": "Colonne: funzionalità 2" }, "Navigation": { "cohortSaved": "La nuova coorte è stata salvata. Vedere l'elenco coorte in Impostazioni coorte.", @@ -433,9 +433,9 @@ "defaultLabelCopy": "Copia di tutti i dati" }, "TreeView": { - "ariaLabel": "Interactive chart", - "disabledArialLabel": "Disabled interactive chart", - "treeMapInfoTitle": "Additional information on tree map", + "ariaLabel": "Grafico interattivo", + "disabledArialLabel": "Grafico interattivo disabilitato", + "treeMapInfoTitle": "Altre informazioni sulla mappa ad albero", "treeDescription": "La visualizzazione ad albero usa le informazioni reciproche tra ogni funzionalità e l'errore per separare meglio gerarchicamente le istanze di errore dalle istanze di esito positivo nei dati. Ciò semplifica il processo di individuazione ed evidenziazione dei modelli di errore comuni. Per trovare modelli di errore importanti, cerca i nodi con un colore rosso più forte (ad esempio, una frequenza di errore elevata) e una linea di riempimento più marcata (ad esempio, copertura degli errori elevata). Per modificare l'elenco delle funzionalità usate nell'albero, fai clic su \" Elenco delle funzionalità.\" Usa il menu a cascata \"seleziona metrica\" per altre informazioni sulle prestazioni dei nodi di errore e di esito positivo. Si noti che questa selezione della metrica non influirà sul modo in cui viene generato l'albero degli errori.", "treeStaticDescription": "La visualizzazione ad albero usa le informazioni reciproche tra ogni funzionalità e l'errore per separare meglio gerarchicamente le istanze di errore dalle istanze di esito positivo nei dati. Ciò semplifica il processo di individuazione ed evidenziazione dei modelli di errore comuni. Per trovare modelli di errore importanti, cerca i nodi con un colore rosso più forte (ad esempio, una frequenza di errore elevata) e una linea di riempimento più marcata (ad esempio, copertura degli errori elevata). Per visualizzare l'elenco delle funzionalità usate nell'albero, fai clic su \" Elenco delle funzionalità.\" Usa il menu a cascata \"seleziona metrica\" per altre informazioni sulle prestazioni dei nodi di errore e di esito positivo. Si noti che questa selezione della metrica non influirà sul modo in cui viene generato l'albero degli errori.", "disabledWarning": "La mappa ad albero degli errori è disabilitata a meno che la coorte globale non venga impostata per rappresentare \"Tutti i dati\" a causa della generazione della mappa ad albero degli errorii per il set di dati completo. Tornare al set di dati completo per visualizzare la mappa ad albero degli errori." @@ -770,7 +770,7 @@ "countHelperText": "Un istogramma del numero di punti", "ditherLabel": "Applica il dithering", "groupByCohort": "Gruppo per coorte", - "logarithmicScaling": "Enable logarithmic scaling", + "logarithmicScaling": "Abilitare la scala logaritmica", "numOfBins": "Numero di bin", "selectClass": "Selezionare classe", "selectFeature": "Selezionare caratteristica", @@ -794,7 +794,7 @@ "importancePrefix": "Importanza", "numberOfDatapoints": "Numero di punti dati", "rowIndex": "Indice di riga", - "absoluteIndex": "Absolute index", + "absoluteIndex": "Indice assoluto", "xValue": "Valore X", "yValue": "Valore Y" }, @@ -822,12 +822,12 @@ }, "CohortEditor": { "columns": { - "index": "Index", - "dataset": "Dataset", - "predictedY": "Predicted Y", - "trueY": "True Y", - "classificationOutcome": "Classification outcome", - "regressionError": "Error" + "index": "Indice", + "dataset": "Set di dati", + "predictedY": "Y stimato", + "trueY": "Y vero", + "classificationOutcome": "Risultato classificazione", + "regressionError": "Errore" }, "TreatAsCategorical": "Gestire per categorie", "addFilter": "Aggiungere filtro", @@ -852,8 +852,8 @@ "save": "Salva", "saveAndSwitch": "Salva e cambia", "selectFilter": "Seleziona filtro", - "noFiltersApplied": "No filters applied", - "filterAdded": "Filter added" + "noFiltersApplied": "Nessun filtro applicato", + "filterAdded": "Filtro aggiunto" }, "Columns": { "classificationOutcome": "Risultato classificazione", @@ -863,8 +863,8 @@ "falsePositive": "Falso positivo", "none": "Conteggio", "predictedProbabilities": "Probabilità di previsione", - "predictedLabels": "Predicted labels", - "trueLabels": "True labels", + "predictedLabels": "Etichette stimate", + "trueLabels": "Etichette true", "regressionError": "Errore regressione", "trueNegative": "Vero negativo", "truePositive": "Vero positivo", @@ -885,7 +885,7 @@ "aggregatePlots": "Aggrega tracciati", "chartType": "Tipo di grafico", "colorValue": "Valore di colore", - "infoTitle": "Additional information on data analysis chart view", + "infoTitle": "Informazioni aggiuntive sulla vista grafico di analisi dei dati", "helperText": "Crea coorti di set di dati per analizzare le statistiche dei set di dati insieme a filtri come i risultati stimati, le funzionalità del set di dati e i gruppi di errori. Informazioni sulla sovra/sottorappresentazione del set di dati.", "individualDatapoints": "Singoli punti dati", "missingParameters": "Questa scheda richiede che sia fornito un set di dati di valutazione.", @@ -906,6 +906,8 @@ "index": "Indice", "output": "Output", "predictedY": "Y stimato", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Probabilità : {0}", "trueY": "Y vero", "xValue": "Valore X:", @@ -974,10 +976,10 @@ "dependencePlotHelperText": "Questo tracciato di dipendenza mostra la relazione tra i valori di una caratteristica e i valori di importanza della caratteristica corrispondente.", "dependencePlotTitle": "Tracciati delle dipendenze", "helperText": "Esplorare le caratteristiche importanti top-k che influiscono sulle stime complessive del modello (noto come spiegazione globale). Usare il dispositivo di scorrimento per visualizzare le importanze delle caratteristiche decrescenti. Tutte le importanze delle caratteristiche delle coorti sono visualizzate affiancate e possono essere disattivate selezionando la coorte nella legenda. Fare clic su una delle caratteristiche del grafo per visualizzare un grafico di densità sottostante, sul modo in cui i valori della caratteristica selezionata influiscono sulla previsione.", - "infoTitle": "Additional information on aggregate feature importance", + "infoTitle": "Informazioni aggiuntive sull'importanza delle funzionalità di aggregazione", "legendHelpText": "Attivare e disattivare le coorti nel tracciato facendo clic sugli elementi della legenda.", "missingParameters": "Questa scheda richiede che sia fornito il parametro di importanza della caratteristica locale.", - "sortByCohort": "Sort by cohort", + "sortByCohort": "Ordina per coorte", "sortBy": "Ordina per punto dati", "topAtoB": "Principali funzionalità {0} per importanza", "viewDependencePlotFor": "Visualizza tracciato delle dipendenze per:", @@ -1020,15 +1022,15 @@ }, "Statistics": { "accuracy": "Accuratezza: {0}", - "bleuScore": "Bleu score: {0}", - "bertScore": "Bert score: {0}", - "exactMatchRatio": "Exact match ratio: {0}", - "rougeScore": "Rouge Score: {0}", + "bleuScore": "Punteggio bleu: {0}", + "bertScore": "Punteggio Bert: {0}", + "exactMatchRatio": "Rapporto di corrispondenza esatto: {0}", + "rougeScore": "Punteggio di Rouge: {0}", "fnr": "Percentuale di falsi negativi: {0}", "fpr": "Percentuale di falsi positivi: {0}", - "hammingScore": "Hamming score: {0}", + "hammingScore": "Punteggio di hamming: {0}", "meanPrediction": "Stima media {0}", - "meteorScore": "Meteor Score: {0}", + "meteorScore": "Punteggio di Meteor: {0}", "mse": "Errore quadratico medio: {0}", "precision": "Precisione: {0}", "rSquared": "R²: {0}", @@ -1036,10 +1038,10 @@ "selectionRate": "Tasso di selezione: {0}", "mae": "Errore assoluto medio: {0}", "f1Score": "Punteggio F1: {0}", - "samples": "Sample size: {0}", - "meanAveragePrecision": "Mean average precision: {0}", - "averagePrecision": "Average precision: {0}", - "averageRecall": "Average recall: {0}" + "samples": "Dimensioni campione: {0}", + "meanAveragePrecision": "Precisione media: {0}", + "averagePrecision": "Precisione media: {0}", + "averageRecall": "Richiamo medio: {0}" }, "ValidationErrors": { "addFilters": "Aggiungere filtri", @@ -1147,30 +1149,30 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Dashboard interpretabilità", - "importantWords": "Show most important words", + "importantWords": "Mostra le parole più importanti", "topFeatureList": "Analisi dell'elenco delle funzionalità principali", "allButton": "TUTTE LE FUNZIONALITÀ", "negButton": "CARATTERISTICHE NEGATIVE", "posButton": "CARATTERISTICHE POSITIVE", - "legendText": "Positive scalar feature importances represent the extent that the words were important towards the classification of your selected label, and negative scalar feature importances represent words that encouraged your model away from your selected label.", - "legendTextForQA": "The left text box and the bar chart display the predictions of the model. The right text box shows the feature importance associated with a selected token. Positive feature importances represent the extent that the words were important towards marking the selected token as the starting/ending position of the answer.", + "legendText": "Le priorità delle caratteristiche scalari positive rappresentano la misura in cui la parola era importante per la classificazione dell'etichetta selezionata e le priorità delle caratteristiche scalari negative rappresentano le parole che hanno allontanato il modello dall'etichetta selezionata.", + "legendTextForQA": "La casella di testo a sinistra e il grafico a barre mostrano le previsioni del modello. La casella di testo a destra mostra l'importanza della caratteristica associata a un token selezionato. Le priorità delle funzionalità positive rappresentano la misura in cui le parole sono importanti per contrassegnare il token selezionato come posizione iniziale o finale della risposta.", "label": "Etichetta", "colon": ":", - "startingPosition": "STARTING POSITION", - "endingPosition": "ENDING POSITION", - "predictedAnswer": "Predicted answer: ", - "trueAnswer": "True answer: ", - "inputs": "Inputs", - "outputs": "Outputs", - "sliderAriaLabel": "Slider for most important words" + "startingPosition": "POSIZIONE INIZIALE", + "endingPosition": "POSIZIONE FINALE", + "predictedAnswer": "Risposta prevista: ", + "trueAnswer": "Risposta vera: ", + "inputs": "Input", + "outputs": "Output", + "sliderAriaLabel": "Dispositivo di scorrimento per le parole più importanti" }, "Legend": { "featureLegend": "LEGENDA FUNZIONALITÀ TESTO", "posFeatureImportance": "IMPORTANZA DELLA CARATTERISTICA POSITIVA", "negFeatureImportance": "IMPORTANZA DELLA CARATTERISTICA NEGATIVA", - "cls": "CLS: start of the sentence", - "sep": "SEP: end of the sentence", - "selectedWord": "Selected word: " + "cls": "CLS: inizio della frase", + "sep": "SEP: fine della frase", + "selectedWord": "Parola selezionata: " }, "BarChart": { "featureImportance": "IMPORTANZA DELLA FUNZIONALITÀ" @@ -1178,59 +1180,59 @@ }, "InterpretVision": { "Cohort": { - "close": "Close", - "errorCohortName": "Please choose a unique cohort name.", - "errorNumSelected": "Please select at least one (1) item.", - "itemsSelectedSingular": "item selected", - "itemsSelectedPlural": "items selected", - "save": "Save cohort", - "saveAndClose": "Save and close", - "saveAndSwitch": "Save and switch", - "textField": "New cohort name", - "title": "Save new cohort" + "close": "Chiudi", + "errorCohortName": "Scegliere un nome di coorte univoco.", + "errorNumSelected": "Selezionare almeno un (1) elemento.", + "itemsSelectedSingular": "elemento selezionato", + "itemsSelectedPlural": "elementi selezionati", + "save": "Salvare coorte", + "saveAndClose": "Salva e chiudi", + "saveAndSwitch": "Salva e cambia", + "textField": "Nuovo nome coorte", + "title": "Salva nuova coorte" }, "Dashboard": { "allData": "Tutti i dati", - "columnOne": "Image", + "columnOne": "Immagine", "columnTwo": "Indice", "columnThree": "Y vero", "columnFour": "Y stimato", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Corretto", + "columnFourOD": "Non corretto", "columnFive": "Altri metadati", - "chooseObject": "Choose a detected object", - "examples": "examples", + "chooseObject": "Scegliere un oggetto rilevato", + "examples": "esempi", "filter": "Filtro", - "indexLabel": "Image ", - "labelTypeDropdown": "Select label type", - "labelVisibilityDropdown": "Select labels to display", - "legendFailure": "failure", - "legendSuccess": "success", - "loading": "Computing explanation for index", - "multiselect": "Multiselect", - "notdefined": "object scenario not defined", - "objectSelect": "Object Selection", + "indexLabel": "Immagine ", + "labelTypeDropdown": "Selezionare il tipo di etichetta", + "labelVisibilityDropdown": "Selezionare le etichette da visualizzare", + "legendFailure": "errore", + "legendSuccess": "operazione completata", + "loading": "Spiegazione di calcolo per l'indice", + "multiselect": "Selezione multipla", + "notdefined": "scenario oggetto non definito", + "objectSelect": "Selezione oggetto", "pageSize": "Dimensioni pagina: ", - "panelTitle": "Selected instance", - "panelExplanation": "Explanation", - "panelInformation": "Information", - "predictedLabel": "Predicted label", - "predictedY": "Predicted: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", - "prefix": "Object: ", - "rows": "Rows: ", + "panelTitle": "Istanza selezionata", + "panelExplanation": "Spiegazione", + "panelInformation": "Informazioni", + "predictedLabel": "Etichetta stimata", + "predictedY": "Stimati: ", + "correctDetections": "Rilevamenti corretti: ", + "incorrectDetections": "Rilevamenti non corretti: ", + "prefix": "Oggetto: ", + "rows": "Righe: ", "search": "Cerca", - "selectAll": "Select all", + "selectAll": "Seleziona tutto", "settings": "Impostazioni", - "showAll": "Show all", + "showAll": "Mostra tutto", "tabOptionFirst": "Visualizzazione Esplora immagini", "tabOptionSecond": "Visualizzazione tabella", - "tabOptionThird": "Class view", + "tabOptionThird": "Visualizzazione classi", "thumbnailSize": "Dimensioni anteprima", "titleBarError": "Esempi di errori", "titleBarSuccess": "Istanze riuscite", - "trueY": "Ground truth: " + "trueY": "Stazione di terra: " } }, "ModelAssessment": { @@ -1239,15 +1241,15 @@ "CalloutContent": "L'aggiunta di alcuni componenti (visualizzazione struttura ad albero degli errori, mappa termica degli errori) consentirà di filtrare i dati della coorte globale visualizzata nei componenti seguenti.", "CalloutTitle": "Aggiungi componente", "TabAddedMessage": { - "DataAnalysis": "Data analysis component added", - "FeatureImportances": "Feature importances component added", - "ErrorAnalysis": "Error analysis component added", - "Fairness": "Fairness component added", - "ModelOverview": "Model overview component added", - "CausalAnalysis": "Causal analysis component added", - "Counterfactuals": "Counterfactuals component added", - "Vision": "Vision data explorer component added", - "Forecasting": "Forecasting what-if component added" + "DataAnalysis": "Componente di analisi dei dati aggiunto", + "FeatureImportances": "Componente delle priorità delle funzionalità aggiunto", + "ErrorAnalysis": "Componente di analisi degli errori aggiunto", + "Fairness": "Aggiunta del componente di equità", + "ModelOverview": "Componente di panoramica del modello aggiunto", + "CausalAnalysis": "Componente di analisi causale aggiunto", + "Counterfactuals": "Componente Counterfactuals aggiunto", + "Vision": "Componente Esplora dati di Visione aggiunto", + "Forecasting": "Previsione del componente di simulazione aggiunto" } }, "CausalAnalysis": { @@ -1275,7 +1277,7 @@ }, "CohortInformation": { "ShiftCohort": "Cambiare coorte", - "SwitchTimeSeries": "Switch time series", + "SwitchTimeSeries": "Cambia serie temporale", "NewCohort": "Nuova coorte", "DataPoints": "Numero di punti dati", "DefaultCohort": " (predefinito)", @@ -1287,7 +1289,7 @@ "CohortSettingsTitle": "Impostazioni coorte" }, "ComponentNames": { - "ChartView": "Chart view", + "ChartView": "Visualizzazione grafico", "CausalAnalysis": "Analisi causale", "Counterfactuals": "Controfattuali", "DataAnalysis": "Analisi dei dati", @@ -1296,10 +1298,10 @@ "ErrorAnalysis": "Analisi degli errori", "Fairness": "Equità", "FeatureImportances": "Importanza delle caratteristiche", - "Forecasting": "Forecasting", + "Forecasting": "Previsione", "ModelOverview": "Panoramica del modello", - "TableView": "Table view", - "VisionTab": "Vision data explorer" + "TableView": "Visualizzazione tabella", + "VisionTab": "Esplorare dati di Visione" }, "DashboardSettings": { "Content": "Questo elenco mostra il layout della dashboard. È possibile filtrare i dati usando il componente di analisi degli errori, da visualizzare nei componenti seguenti.", @@ -1458,16 +1460,16 @@ "GlobalExplanation": "Importanza delle caratteristiche aggregate", "IncorrectPredictions": "Stime non corrette", "InfoTitle": "Additional information on feature importance values", - "IndividualFeatureTabular": "Select a datapoint by clicking on a datapoint (up to 5 datapoints) in the table to view their local feature importance values (local explanation) and individual conditional expectation (ICE) plots.", + "IndividualFeatureTabular": "Seleziona un punto dati facendo clic su un punto dati (fino a 5 punti dati) nella tabella per visualizzare i valori di importanza delle funzionalità locali (spiegazione locale) e il grafico ICE (Individual Conditional Expectation).", "IndividualFeatureText": "Select a datapoint by clicking on a datapoint in the table to view the local feature importance values (local explanation) from SHAP's text explainer.", "LocalExplanation": "Importanza della singola caratteristica", "SelectionCounter": "{0}/{1} punti dati selezionati", "SelectionLimit": "Al momento è possibile selezionare fino a 5 punti dati.", - "RowCheckboxAriaLabel": "Row checkbox", - "SelectionColumnAriaLabel": "Toggle selection" + "RowCheckboxAriaLabel": "Casella di controllo della riga", + "SelectionColumnAriaLabel": "Attiva/Disattiva la selezione" }, "IndividualFeatureImportanceView": { - "SmallInstanceSelection": "Instance selection" + "SmallInstanceSelection": "Selezione istanza" }, "MainMenu": { "DashboardSettings": "Configurazione della dashboard", @@ -1483,44 +1485,44 @@ "ModelOverview": { "metrics": { "accuracy": { - "name": "Accuracy score", + "name": "Punteggio di accuratezza", "description": "Frazione dei punti dati classificati correttamente." }, "exactMatchRatio": { - "name": "Exact match ratio", - "description": "The ratio of instances classified correctly for every label." + "name": "Rapporto di corrispondenza esatto", + "description": "Rapporto delle istanze classificate correttamente per ogni etichetta." }, "meteorScore": { - "name": "Meteor Score", - "description": "METEOR Score is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision in question answering task." + "name": "Punteggio di Meteor", + "description": "Il punteggio METEOR viene calcolato in base alla media armonica di precisione e richiamo, con richiamo ponderato più della precisione nell'attività di risposta in questione." }, "bleuScore": { - "name": "Bleu Score", - "description": "Bleu Score measures the ratio of words (and/or n-grams) in the machine generated text that appeared in the reference text in question answering task." + "name": "Punteggio bleu", + "description": "Il punteggio bleu misura il rapporto tra le parole (e/o n-grammi) nel testo generato dalla macchina che compare nel testo di riferimento nell'attività di risposta in questione." }, "bertScore": { - "name": "Bert Score", - "description": "BERTScore focuses on computing semantic similarity between tokens of reference and machine generated text in question answering task." + "name": "Punteggio Bert", + "description": "BERTScore si concentra sul calcolo della somiglianza semantica tra token di riferimento e testo generato dal computer nell'attività di risposta alle domande." }, "rougeScore": { - "name": "Rouge Score", - "description": "Rouge Score measures the ratio of words (and/or n-grams) in the reference text that appeared in the machine generated text in question answering task." + "name": "Punteggio di Rouge", + "description": "Il punteggio Rouge misura il rapporto tra le parole (e/o n-grammi) nel testo di riferimento generato che compare nel testo generato dal computer nell'attività di risposta in questione." }, "hammingScore": { - "name": "Hamming score", - "description": "The average ratio of labels classified correctly among those classified as 1 in multilabel task." + "name": "Punteggio di hamming", + "description": "Rapporto medio delle etichette classificate correttamente tra quelle classificate come 1 nell'attività multi-etichetta." }, "f1Score": { "name": "Punteggio F1", "description": "Il punteggio F1 è la media armonica di precisione e richiamo." }, "f1ScoreMacro": { - "name": "Macro F1 score", - "description": "Macro F1 score is the harmonic mean of precision and recall for each class, with each class weighted equally." + "name": "Punteggio F1 macro", + "description": "Il punteggio della macro F1 è la media armonica di precisione e richiamo per ogni classe, con ogni classe ponderata in modo uniforme." }, "f1ScoreMicro": { - "name": "Micro F1 score", - "description": "Micro F1 score is the harmonic mean of precision and recall for each class, with each class weighted according to how many instances it contains." + "name": "Punteggio Micro F1", + "description": "Il punteggio Micro F1 è la media armonica di precisione e richiamo per ogni classe, con ogni classe ponderata in base al numero di istanze che contiene." }, "meanAbsoluteError": { "name": "Errore assoluto medio", @@ -1535,24 +1537,24 @@ "description": "Frazione dei punti dati classificati correttamente tra quelli classificati come 1." }, "precisionMacro": { - "name": "Macro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted equally." + "name": "Punteggio di precisione macro", + "description": "Frazione dei punti dati classificati correttamente tra quelli la cui etichetta vera è 1 per ogni classe con ogni classe ponderata equamente." }, "precisionMicro": { - "name": "Micro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted according to how many instances it contains." + "name": "Punteggio di precisione micro", + "description": "Frazione dei punti dati classificati correttamente tra quelli classificati come 1 per ogni classe con ogni classe ponderata in base al numero di istanze che contiene." }, "recall": { "name": "Punteggio di richiamo", "description": "Frazione dei punti dati classificati correttamente tra quelli la cui etichetta vero è 1. Nomi alternativi: percentuale di veri positivi, sensibilità." }, "recallMacro": { - "name": "Macro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted equally." + "name": "Punteggio richiamo macro", + "description": "Frazione dei punti dati classificati correttamente tra quelli la cui etichetta vera è 1 per ogni classe con ogni classe ponderata equamente." }, "recallMicro": { - "name": "Micro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted according to how many instances it contains." + "name": "Punteggio richiamo macro", + "description": "Frazione dei punti dati classificati correttamente tra quelli la cui etichetta vera è 1 per ogni classe con ogni classe ponderata in base al numero di istanze che contiene." }, "falsePositiveRate": { "name": "Percentuale di falsi positivi", @@ -1571,32 +1573,32 @@ "description": "Media di tutte le stime." }, "meanAveragePrecision": { - "name": "Mean Average Precision score", - "description": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall." + "name": "Punteggio medio di precisione media", + "description": "La precisione media per i modelli di rilevamento oggetti è la media di AP (precisione media) in tutte le classi. Ciò valuta l'affidabilità del modello di rilevamento oggetti e incapsula il compromesso tra precisione e richiamo." }, "averagePrecision": { - "name": "Average Precision score", - "description": "Average precision for object detection models is calculated for a selected class." + "name": "Punteggio di precisione media", + "description": "La precisione media per i modelli di rilevamento oggetti viene calcolata per una classe selezionata." }, "averageRecall": { - "name": "Average Recall score", - "description": "Average recall for object detection models is calculated for a selected class." + "name": "Punteggio di richiamo medio", + "description": "La precisione media per i modelli di rilevamento oggetti viene calcolata per una classe selezionata." }, "fairnessMetricDifference": "Differenza", "fairnessMetricRatio": "Proporzioni" }, "metricsDropdown": "Metriche", - "metricsTypeDropdown": "Aggregate method", + "metricsTypeDropdown": "Metodo di aggregazione", "metricTypes": { "macro": "Macro", "micro": "Micro" }, - "classSelectionDropdown": "Select class(es)", + "classSelectionDropdown": "Selezionare le classi", "iouThresholdDropdown": { - "name": "IoU Threshold", - "description": "Intersection over Union quantifies the degree of overlap between the prediction and ground truth bounding box of a detected object in an image. For example, setting an IoU threshold of 70% means that a prediction with greater than 70% overlap with ground truth is True, thus influencing the definition of prediction correctness and calculation of other performance metrics.", + "name": "Soglia IOU", + "description": "L'intersezione sull'unione quantifica il grado di sovrapposizione tra il rettangolo di delimitazione della stima e della verità di terra di un oggetto rilevato in un'immagine. Ad esempio, l'impostazione di una soglia IoU del 70% significa che una stima con una sovrapposizione maggiore del 70% con la verità di base è True, influenzando così la definizione della correttezza della stima e il calcolo di altre metriche delle prestazioni.", "iconId": "iouThresholdIconId", - "title": "Learn about the IoU threshold" + "title": "Informazioni sulla soglia ioU" }, "notAvailable": "N/D", "countColumnHeader": "Dimensioni del campione", @@ -1608,14 +1610,14 @@ "featuresDropdown": "Caratteristiche", "metricChartDropdownSelectionHeader": "Metrica", "probabilityForClassSelectionHeader": "Probabilità per la classe", - "targetSelectionHeader": "Target", + "targetSelectionHeader": "Destinazione", "metricSelectionDropdownPlaceholder": "Seleziona le metriche per confrontare le coorti.", - "classSelectionDropdownPlaceholder": "Select class name for class-based analysis.", + "classSelectionDropdownPlaceholder": "Selezionare il nome della classe per l'analisi basata su classi.", "featureSelectionDropdownPlaceholder": "Seleziona le funzionalità da usare per un'analisi basata sulle funzionalità.", "probabilityDistributionPivotItem": "Distribuzione di probabilità", - "regressionDistributionPivotItem": "Target distribution", + "regressionDistributionPivotItem": "Distribuzione di destinazione", "metricsVisualizationsPivotItem": "Visualizzazioni delle metriche", - "confusionMatrixPivotItem": "Confusion matrix", + "confusionMatrixPivotItem": "Matrice di confusione", "disaggregatedAnalysisFeatureSelectionPlaceholder": "Seleziona le funzionalità per generare l'analisi basata sulle funzionalità.", "tableCountTooltip": "Coorte {0} contiene {1} istanze.", "tableMetricTooltip": "La classe {0} nella coorte {1} è {2}", @@ -1626,36 +1628,36 @@ "metricSelectionButton": "Scegli la metrica", "cohortSelectionButton": "Scegli coorti", "probabilityLabelSelectionButton": "Scegli etichetta", - "regressionTargetSelectionButton": "Choose target", + "regressionTargetSelectionButton": "Scegli destinazione", "selectAllCohortsOption": "Seleziona tutto", "other": "Altro", "BoxPlot": { "outlierProbability": "probabilità", "outlierLabel": "Outlier", "boxPlotSeriesLabel": "Box plot", - "lowerWhisker": "Lower whisker", - "upperWhisker": "Upper whisker", - "median": "Median", - "lowerQuartile": "Lower quartile", - "upperQuartile": "Upper quartile" + "lowerWhisker": "Baffi inferiori", + "upperWhisker": "Baffi superiori", + "median": "Mediana", + "lowerQuartile": "Quartile inferiore", + "upperQuartile": "Quartile superiore" }, "chartConfigApply": "Applica", "chartConfigCancel": "Annulla", "chartConfigDatasetCohortSelectionPlaceholder": "Seleziona le coorti del set di dati", "chartConfigFeatureBasedCohortSelectionPlaceholder": "Selezionare coorti basate su funzionalità", "confusionMatrix": { - "confusionMatrixCohortSelectionLabel": "Select dataset cohort", - "confusionMatrixClassSelectionLabel": "Select classes", - "confusionMatrixClassMinSelectionError": "Select at least {0} classes to visualize the confusion matrix.", - "confusionMatrixClassMaxSelectionError": "Select at most {0} classes to visualize the confusion matrix.", - "confusionMatrixClassSelectionDefaultPlaceholder": "Choose classes", - "confusionMatrixHeatmapTooltip": "{0} datapoints should be {1}, predicted to be {2}", - "confusionMatrixYAxisLabel": "True Class", - "confusionMatrixXAxisLabel": "Predicted Class", - "class": "Class" + "confusionMatrixCohortSelectionLabel": "Seleziona coorte del set di dati", + "confusionMatrixClassSelectionLabel": "Selezionare le classi", + "confusionMatrixClassMinSelectionError": "Selezionare almeno {0} classi per visualizzare la matrice di confusione.", + "confusionMatrixClassMaxSelectionError": "Selezionare al massimo {0} classi per visualizzare la matrice di confusione.", + "confusionMatrixClassSelectionDefaultPlaceholder": "Scegliere classi", + "confusionMatrixHeatmapTooltip": "{0} i punti dati devono essere {1}, stimati come {2}", + "confusionMatrixYAxisLabel": "Classe effettiva", + "confusionMatrixXAxisLabel": "Classe stimata", + "class": "Classe" }, "nA": "N/D", - "disaggregatedAnalysisBaseCohortDisclaimer": "The cohorts in the following feature-based analysis are based on the global cohort, {0}.", + "disaggregatedAnalysisBaseCohortDisclaimer": "Le coorti nell'analisi basata sulle funzionalità seguenti si basano sulla coorte globale, {0}.", "disaggregatedAnalysisBaseCohortWarning": "A differenza della coorte {0}, {1} include filtri. Di conseguenza, acquisisce solo un subset dell'intero set di dati e le informazioni dettagliate potrebbero non generalizzare il set di dati completo.", "probabilitySplineChartToggleLabel": "Usa il grafico spline", "countAxisLabel": "Conteggio", @@ -1685,76 +1687,76 @@ "flyoutDescription": "È possibile scegliere di visualizzare coorti di set di dati o coorti di funzionalità. Se le coorti di funzionalità non sono disponibili, sarà necessario selezionare prima una o più funzionalità nella visualizzazione coorti di funzionalità. Successivamente, verranno generate le coorti di funzionalità che sarà possibile selezionate qui." }, "regressionTargetOptions": { - "predictedY": "Predicted Y", - "trueY": "True Y", - "error": "Error" + "predictedY": "Y stimato", + "trueY": "Y vero", + "error": "Errore" }, "topLevelDescription": "Valuta le prestazioni del modello esplorando la distribuzione dei valori di stima e i valori delle metriche delle prestazioni del modello. Usa le \"coorti del set di dati” per analizzare il modello, grazie a un'analisi comparata delle prestazioni in diverse coorti di set di dati predefinite o appena create. Usa \" Coorti di funzionalità\" per analizzare il modello esaminando un'analisi comparata delle prestazioni in sottocoorti di funzionalità sensibili/non sensibili. (ad esempio, prestazioni tra generi diversi, livelli di reddito).", - "infoTitle": "Additional information on model overview", + "infoTitle": "Informazioni aggiuntive sulla panoramica del modello", "visualDisplayToggleLabel": "Mostra mappa termica", "featureBasedViewDescription": "Seleziona fino a due funzionalità per visualizzare la suddivisione delle prestazioni del modello tra coorti basate su funzionalità (se è selezionata una funzionalità) o coorti intersezionali (se sono selezionate due funzionalità)." }, "TableViewTab": { - "Heading": "View the dataset in a table format for all features and rows." + "Heading": "Consente di visualizzare il set di dati in un formato tabella per tutte le funzionalità e le righe." } }, "Forecasting": { - "target": "Target", - "whatIfForecastingHeader": "What-if analysis", - "forecastHeader": "Forecast analysis", - "whatIfForecastingDescription": "What-if allows you to perturb features for your entire time series and observe how the model's forecast changes.", - "whatIfForecastingChooseTimeSeries": "To start, choose a time series from the options below.", - "forecastDescription": "Forecast analysis compares your model's forecast to the actual values of your time series. To enable what-if analysis, provide a dataset with features.", - "timeSeries": "Time series", - "selectTimeSeries": "Select a time series.", - "singleTimeSeries": "The dataset contains only a single time series '{0}' which has been selected by default.", - "trueY": "True Y", - "baselinePrediction": "Baseline prediction", - "forecastComparisonHeader": "Compare What-if Forecasts", - "forecastComparisonChartTitle": "Forecasts", - "forecastComparisonChartTimeAxisLabel": "Time", + "target": "Destinazione", + "whatIfForecastingHeader": "Analisi di simulazione", + "forecastHeader": "Analisi delle previsioni", + "whatIfForecastingDescription": "Si tratta di un'opzione che consente di rendere effettive le funzionalità per l'intera serie temporale e di osservare le variazioni delle previsioni del modello.", + "whatIfForecastingChooseTimeSeries": "Per iniziare, scegliere una serie temporale tra le opzioni seguenti.", + "forecastDescription": "L'analisi delle previsioni confronta la previsione del modello con i valori effettivi delle serie temporali. Per abilitare l'analisi di simulazione, fornire un set di dati con funzionalità.", + "timeSeries": "Serie temporale", + "selectTimeSeries": "Selezionare una serie temporale.", + "singleTimeSeries": "Il set di dati contiene una sola serie temporale '{0}' che è stata selezionata per impostazione predefinita.", + "trueY": "Y vero", + "baselinePrediction": "Previsione prevista", + "forecastComparisonHeader": "Confrontare le previsioni di simulazione", + "forecastComparisonChartTitle": "Previsioni", + "forecastComparisonChartTimeAxisLabel": "Durata", "Transformations": { - "multiply": "multiply", - "divide": "divide", - "add": "add", - "subtract": "subtract", - "change": "change to" + "multiply": "moltiplicazione", + "divide": "divisione", + "add": "addizione", + "subtract": "sottrazione", + "change": "cambia in" }, "TransformationCreation": { - "title": "Create what-if scenario", - "nameLabel": "What-if scenario name", - "featureInstructions": "Choose a feature to perturb.", - "operationInstructions": "Choose an operation to apply to the feature.", - "operationDropdownHeader": "Operation", - "featureDropdownHeader": "Feature", - "valueSpinButtonHeader": "Value", - "scenarioNamingInstructionsPlaceholder": "Enter a unique name", - "scenarioNamingInstructions": "Enter a name for your what-if scenario.", - "scenarioNamingCollisionMessage": "This name exists already. Please enter a unique name.", - "scenarioNamingLengthMessage": "The name must be between 1 and 50 characters. The actual length is {0}.", - "scenarioNamingInvalidCharactersMessage": "The name can only contain alphanumeric characters, whitespaces, dashes, or underscores, and needs to start with an alphanumeric character.", - "valueErrorMessage": "For operation {0} please select a value other than {1}.", - "invalidCombinationErrorMessage": "This is identical to an existing what-if scenario. Please change the feature, operation, or value.", - "addTransformationButton": "Add Transformation", - "divisionAndMultiplicationBy": "by" + "title": "Creare uno scenario di simulazione", + "nameLabel": "Nome scenario di simulazione", + "featureInstructions": "Scegliere una funzionalità da rendere disponibile.", + "operationInstructions": "Scegliere un'operazione da applicare alla funzionalità.", + "operationDropdownHeader": "Operazione", + "featureDropdownHeader": "Caratteristica", + "valueSpinButtonHeader": "Valore", + "scenarioNamingInstructionsPlaceholder": "Immetti un nome univoco", + "scenarioNamingInstructions": "Immettere un nome per lo scenario di simulazione.", + "scenarioNamingCollisionMessage": "Questo nome esiste già. Immettere un nome univoco.", + "scenarioNamingLengthMessage": "Il nome deve avere una lunghezza compresa tra 1 e 50 caratteri. La lunghezza effettiva è {0}.", + "scenarioNamingInvalidCharactersMessage": "Il nome può contenere solo caratteri alfanumerici, spazi vuoti, trattini o caratteri di sottolineatura e deve iniziare con un carattere alfanumerico.", + "valueErrorMessage": "Per l'operazione {0} selezionare un valore diverso da {1}.", + "invalidCombinationErrorMessage": "È identico a uno scenario di simulazione esistente. Modificare la funzionalità, l'operazione o il valore.", + "addTransformationButton": "Aggiungi trasformazione", + "divisionAndMultiplicationBy": "di" }, "TransformationTable": { - "nameColumnHeader": "Name", - "methodColumnHeader": "Method", - "divisionAndMultiplicationBy": "by ", - "header": "What-if Forecasts ({0})" + "nameColumnHeader": "Nome", + "methodColumnHeader": "Metodo", + "divisionAndMultiplicationBy": "di ", + "header": "Previsioni di simulazione ({0})" }, "TimeSeries": { - "apply": "Apply", - "cancel": "Cancel", - "cohortList": "Time series list", - "selectCohort": "Select a time series", - "shiftCohort": "Switch time series", - "shiftCohortDescription": "Select a time series from the time series list. Apply the time series to the dashboard." + "apply": "Applica", + "cancel": "Annulla", + "cohortList": "Elenco serie temporali", + "selectCohort": "Selezionare una serie temporale", + "shiftCohort": "Cambiare serie temporale", + "shiftCohortDescription": "Selezionare una serie temporale dall'elenco delle serie temporali. Consente di applicare la serie temporale al dashboard." }, "TimeSeriesSettings": { - "CohortSettingsDescription": "Time series are pre-defined based on time series identifying columns.", - "CohortSettingsTitle": "Time series settings" + "CohortSettingsDescription": "Le serie temporali sono predefinite in base alle serie temporali che identificano le colonne.", + "CohortSettingsTitle": "Impostazioni serie temporali" } } } \ No newline at end of file diff --git a/libs/localization/src/lib/en.ja.json b/libs/localization/src/lib/en.ja.json index c55145c0eb..8b8f581303 100644 --- a/libs/localization/src/lib/en.ja.json +++ b/libs/localization/src/lib/en.ja.json @@ -3,26 +3,26 @@ "close": "閉じる", "tooltipButton": "ツールヒント ボタン", "identityFeature": "ID 特徴", - "infoTitle": "Additional information", - "spinButton": "Spin", - "editButton": "Edit", - "decreaseValue": "Decrease value", - "increaseValue": "Increase value", - "decreaseValueByOne": "Decrease value by 1", - "increaseValueByOne": "Increase value by 1", - "loading": "Loading..." + "infoTitle": "追加情報", + "spinButton": "スピン", + "editButton": "編集", + "decreaseValue": "値を減らす", + "increaseValue": "値を増やす", + "decreaseValueByOne": "値を 1 減らす", + "increaseValueByOne": "値を 1 増やす", + "loading": "読み込んでいます..." }, "ChartContextMenu": { - "hideData": "Hide data table", - "viewData": "View data table", - "viewInFullScreen": "View in full screen", - "printChart": "Print chart", - "downloadCSV": "Download CSV", - "downloadPNG": "Download PNG image", - "downloadJPEG": "Download JPEG image", - "downloadPDF": "Download PDF document", - "downloadSVG": "Download SVG vector image", - "downloadXLS": "Download XLS" + "hideData": "データ テーブルを非表示にする", + "viewData": "データ テーブルの表示", + "viewInFullScreen": "全画面で表示する", + "printChart": "グラフを印刷する", + "downloadCSV": "CSV のダウンロード", + "downloadPNG": "PNG 画像のダウンロード", + "downloadJPEG": "JPEG 画像のダウンロード", + "downloadPDF": "PDF ドキュメントのダウンロード", + "downloadSVG": "SVG ベクター 画像のダウンロード", + "downloadXLS": "XLS のダウンロード" }, "CausalAnalysis": { "AggregateView": { @@ -39,7 +39,7 @@ "description": "因果分析では、製品に関する別の価格戦略や、患者に対して別の処理を行うなどのさまざまなポリシーの選択肢の下で、実世界の結果がどのように変化するかについての “What If” の質問の回答を提供します。重要な相関パターンを識別するモデル予測とは異なり、これらのツールは、興味のある結果に直接影響する最も重要な因果関係の機能を識別するのに役立ちます。これらのモデルは、1 つの機能の因果効果 (通常は “処理” と呼ばれる) を識別し、他の交絡機能を一定に保ちます。最善の結果を得るには、データセット全体に、結果と関連する可能性のある、あらゆる機能を交絡因子が利用できることを確認してください。", "directAggregate": "信頼区間が 95% の各処理の直接的な集計因果効果", "here": "こちら", - "infoTitle": "Additional information on aggregated causal effects", + "infoTitle": "集計された因果効果に関する追加情報", "lasso": "なげなわ (または y がバイナリの場合はロジスティック回帰) が X[-i] から y を予測するために調整され、なげなわ (または X[i] がカテゴリ別の場合はロジスティック回帰) が X[-i] から X[i] を予測するために調整されました。因果効果は、2 つの予測タスクの残余または原因不明のバリエーションの相関関係と見なすことができます。Double Machine Learning に関する詳細情報", "unconfounding": "交絡因子とは" }, @@ -51,7 +51,7 @@ "description": "個別の因果効果は、顧客へのターゲット プロモーションや個別の処理計画など、カスタマイズされた介入に反映されます。特定の特徴量を持つ個人が、因果関係のある特徴量や処理を変更した場合、どのように応答するでしょうか。因果関係の What-If ツールは、処理のレベルを変更した場合の、特定の個人の現実世界の結果の限界的な変化を計算します。この分析により、製品の異なる価格戦略や患者の代替処理など、さまざまなポリシーの選択肢の下で、現実の結果がどのように変化するか理解することができます。興味のある処理を指定して、現実の結果がどのように変化するかを観察します。", "directIndividual": "信頼区間が 95% の各処理の直接的な個別の因果効果", "index": "データポイント インデックス", - "infoTitle": "Additional information on individual causal what-if", + "infoTitle": "個々の因果 what-if に関する追加情報", "missingParameters": "このタブには、評価データセットを指定する必要があります。", "newOutcome": "新しい結果", "selectTreatment": "処理の選択", @@ -85,7 +85,7 @@ "averageGainBinary": "処理 {0} をその初期値 {1} に設定した場合の平均ゲイン。", "averageGainContinuous": "'{0}' 処理を行っていない場合の代替ポリシーの平均利得。", "header": "これらのツールは、将来の介入に向けたポリシーを構築するのに役立ちます。サンプルのどの部分が、原因となる特徴量や処理の変化に対して最大の反応を示すかを特定し、将来どの母集団を特定の介入の対象とすることが必要か定義するルールを構築することができます。", - "infoTitle": "Additional information on treatment policy", + "infoTitle": "処置ポリシーに関する追加情報", "nSample": "n = {0}", "noData": "データなし" } @@ -116,8 +116,8 @@ "cancel": "キャンセル", "title": "コーホートの切り替え", "subText": "コーホート リストでコーホートを選択します。デジタル ダッシュボードにコーホートを適用します。", - "selectCohort": "Select a cohort", - "cohortList": "Cohort list" + "selectCohort": "コーホートの選択", + "cohortList": "コーホート リスト" }, "PreBuiltCohort": { "featureNameNotFound": "データセットに特徴名が見つかりません", @@ -148,13 +148,13 @@ "predictedClass": "予測されたクラス", "predictedValue": "予測された値" }, - "Size": "Size", - "loading": "Loading...", + "Size": "サイズ", + "loading": "読み込み中...", "counterfactualEx": "反事実条件の例 {0}", "counterfactualName": "What-if 反事実条件名", "createWhatIfCounterfactual": "What-if 反事実条件の作成", "createCounterfactual": "反事実条件", - "revertToBubbleChart": "View bubble chart", + "revertToBubbleChart": "バブル チャートの表示", "createOwn": "自分自身の反事実条件を作成:", "currentClass": "現在のクラス", "currentRange": "現在の範囲", @@ -167,9 +167,9 @@ "listDescription": "この一覧では、推定された因果モデルに含まれるすべての特徴量に基づいて、選択された処理に対する最大の因果反応を含む、現在のデータ サンプル内のデータポイントを示します。左側の 5 列は、処理の観察が推奨されているか、現在の処理、処理で推定される効果 (バイナリー処理の場合は処理の行われていないベースラインから処理を適用した効果、または処理の特徴量をサンプルでの通常処理サイズの 10% まで増加または減少させた場合の効果。[ダイナミック: 利用した処理の数値変化を報告するもの］)、およびこの効果の信頼区間 (CI) の上下を示します。残りの列は、それぞれの観察の現在の処理状態とその他の特徴量を示します。", "localImportanceDescription": "目的のモデル予測を実現するために調整する {0} 行の上位の特徴量。予測のための What-if 分析に基づいています: {1}", "localImportanceSelectData": "ローカルの重要度グラフを表示するデータ ポイントを選択します", - "largeLocalImportanceSelectData": "Select a bubble, followed by a data point to view local importance chart", - "localImportanceFetchError": "There was an error while fetching the local importance data. Error details: {0} Please check the data used.", - "BubbleChartFetchError": "There was an error while fetching the data. Error details: {0} Please check the data used.", + "largeLocalImportanceSelectData": "まずバブルを選択し、次にそのローカルの重要度グラフを表示するデータ ポイントを選択します", + "localImportanceFetchError": "ローカルの重要度データのフェッチ中にエラーが発生しました。エラーの詳細: {0}使用されているデータをチェックしてください。", + "BubbleChartFetchError": "データのフェッチ中にエラーが発生しました。エラーの詳細: {0}使用されているデータをチェックしてください。", "noData": "データなし", "noFeatures": "利用できる特徴量がない", "panelDescription": "反事実条件を参照して独自のものを作成します。特徴を検索して、さまざまな反事実条件の例から提案された値を確認します。各反事実条件名の [値の設定] テキストをクリックして、提案された反事実条件の特徴量の値を設定します。反事実条件に名前をつけて保存します。", @@ -223,13 +223,13 @@ "subText": "選択したコーホートの詳細を確認します。そのコーホート名を編集します。このコーホートを削除します。" }, "FeatureList": { - "featureList": "Feature List", + "featureList": "機能の一覧", "apply": "適用", "features": "特徴量", "importances": "重要度", "treeMapDescription": "ツリー マップを再トレーニングするには、以下の特徴量を選択して保存します。特徴量の重要度は相互情報を使用して計算されましたが、true ラベルにエラーが見つかりました。ツリー マップのトレーニングのガイドラインとして使用してください。", "staticTreeMapDescription": "ツリー マップのトレーニングに使用された特徴を表示します。特徴量の重要度は、真のラベルにエラーがある相互情報を使用して計算されました。", - "searchResultMessage": "Results displayed out of {resultLength} for {searchValue}" + "searchResultMessage": "{searchValue}の{resultLength}外に表示される結果" }, "TreeViewParameters": { "maximumDepth": "最大深度", @@ -295,7 +295,7 @@ "disabledWarning": "データセット全体に対してヒートマップが生成されているため、グローバル コーホートが \"すべてのデータ\" を表すように切り替えられる場合を除き、エラー ヒートマップは無効になります。エラー ヒートマップを表示するには、完全なデータセットに切り替えます。" }, "MatrixSummary": { - "heatMapInfoTitle": "Additional information on heat map", + "heatMapInfoTitle": "ヒート マップに関する追加情報", "heatMapDescription": "ヒート マップを使用すると、特定の交差する特徴量のフィルターに注目し、細分化したエラー率を組み合わせることができます。比較する 2 つのデータセット特徴量から開始します。", "heatMapStaticDescription": "ヒート マップを使用すると、特定の交差する特徴量フィルターに注目し、集計解除されたエラー率を計算できます。ダッシュボードを表示する前に、SDK を使用してヒート マップを作成するには、最大 2 つの機能を選択する必要があります。" }, @@ -311,108 +311,108 @@ }, "Metrics": { "AccuracyScore": { - "Name": "Accuracy score", - "Info": "The accuracy score represents the ratio of correct to total instances in the data.", - "Short": "Accuracy", - "Title": "Additional information on accuracy score" + "Name": "正確性スコア", + "Info": "正確性スコアは、データ内のインスタンス総数に対する正しい比率を表します。", + "Short": "正確性", + "Title": "正確性スコアに関する追加情報" }, "ErrorRate": { - "Name": "Error rate", - "Info": "The error rate represents the percentage of instances in the node for which the system has failed.", - "Short": "Error rate", - "Title": "Additional information on error rate" + "Name": "エラー率", + "Info": "エラー率は、システムが失敗したノード内のインスタンスの割合を表します。", + "Short": "エラー率", + "Title": "エラー率に関する追加情報" }, "F1Score": { - "Name": "F1 score", - "Info": "The F1 score is the harmonic mean of the precision and recall metrics.", - "Short": "F1 score", - "Title": "Additional information on F1 score" + "Name": "F1 スコア", + "Info": "F1 スコアは精度と再現メトリックの調和平均です。", + "Short": "F1 スコア", + "Title": "F1 スコアに関する追加情報" }, "MeanAbsoluteError": { - "Name": "Mean absolute error", - "Info": "The mean absolute error is the average of the sum of the errors.", - "Short": "Mean abs. error", - "Title": "Additional information on mean absolute error" + "Name": "平均絶対誤差", + "Info": "平均絶対誤差は、エラーの合計数の平均です。", + "Short": "平均絶対誤差", + "Title": "平均絶対誤差に関する追加情報" }, "MeanSquaredError": { - "Name": "Mean squared error", - "Info": "The mean squared error is the average of the squares of the errors.", - "Short": "Mean sq. error", - "Title": "Additional information on mean squared error" + "Name": "平均二乗誤差", + "Info": "平均二乗誤差は、エラーの二乗の平均です。", + "Short": "平均二乗誤差", + "Title": "平均二乗誤差に関する追加情報" }, "Precision": { - "Name": "Precision score", - "Info": "The precision is the ratio of true positives over all predicted positives.", - "Short": "Precision", - "Title": "Additional information on precision" + "Name": "精度スコア", + "Info": "精度は、すべての予測された陽性に対する真陽性の比率です。", + "Short": "精度", + "Title": "精度に関する追加情報" }, "Recall": { - "Name": "Recall score", - "Info": "The recall is the ratio of true positives over all actual positives.", - "Short": "Recall", - "Title": "Additional information on recall" + "Name": "リコール スコア", + "Info": "リコールは、すべての実際の陽性に対する真陽性の比率です。", + "Short": "リコール", + "Title": "リコールに関する追加情報" }, "MacroPrecision": { - "Name": "Macro averaged precision score", - "Info": "The macro averaged precision is the ratio of true positives over all predicted positives computed independently per class and averaged.", - "Short": "Macro precision", - "Title": "Additional information on macro averaged precision" + "Name": "マクロ平均精度のスコア", + "Info": "マクロ平均精度は、クラスごとに個別に計算され平均化された、すべての予測された陽性に対する真陽性の比率です。", + "Short": "マクロ精度", + "Title": "マクロ平均精度に関する追加情報" }, "MicroPrecision": { - "Name": "Micro averaged precision score", - "Info": "The micro averaged precision is the ratio of true positives over all predicted positives aggregated for all classes.", - "Short": "Micro precision", - "Title": "Additional information on micro averaged precision" + "Name": "マイクロ平均精度のスコア", + "Info": "マイクロ平均精度は、すべてのクラスについて集計されたすべての予測された陽性に対する真陽性の比率です。", + "Short": "マイクロ精度", + "Title": "マイクロ平均精度に関する追加情報" }, "MacroRecall": { - "Name": "Macro averaged recall score", - "Info": "The macro averaged recall is the ratio of true positives over all actual positives computed independently per class and averaged.", - "Short": "Macro recall", - "Title": "Additional information on macro averaged recall" + "Name": "マクロ平均リコールのスコア", + "Info": "マクロ平均リコールは、クラスごとに個別に計算され平均化された、すべての実際の陽性に対する真陽性の比率です。", + "Short": "マクロ リコール", + "Title": "マクロ平均リコールに関する追加情報" }, "MicroRecall": { - "Name": "Micro averaged recall score", - "Info": "The micro averaged recall is the ratio of true positives over all actual positives aggregated for all classes.", - "Short": "Micro recall", - "Title": "Additional information on micro averaged recall" + "Name": "マイクロ平均リコールのスコア", + "Info": "マイクロ平均リコールは、すべてのクラスについて集計されたすべての実際の陽性に対する真陽性の比率です。", + "Short": "マイクロ リコール", + "Title": "マイクロ平均リコールに関する追加情報" }, "MacroF1Score": { - "Name": "Macro averaged F1 score", - "Info": "The macro averaged F1 score is the harmonic mean of the macro averaged precision and recall metrics.", - "Short": "Macro F1 score", - "Title": "Additional information on macro averaged F1 score" + "Name": "マクロ平均 F1 スコア", + "Info": "マクロ平均 F1 スコアは、マクロ平均精度と再現率のメトリックの調和平均です。", + "Short": "マクロ F1 スコア", + "Title": "マクロ平均 F1 に関する追加情報" }, "MicroF1Score": { - "Name": "Micro averaged F1 score", - "Info": "The micro averaged F1 score is the harmonic mean of the micro averaged precision and recall metrics.", - "Short": "Micro F1 score", - "Title": "Additional information on micro averaged F1 score" + "Name": "マイクロ平均 F1 スコア", + "Info": "マイクロ平均 F1 スコアは、マイクロ平均精度と再現率のメトリックの調和平均です。", + "Short": "マイクロ F1 スコア", + "Title": "マイクロ平均 F1 に関する追加情報" }, "MeanAveragePrecision": { - "Name": "Mean average precision score", - "Info": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall.", - "Short": "Mean avg precision", - "Title": "Additional information on mean average precision score" + "Name": "平均適合率スコア", + "Info": "オブジェクト検出モデルにおける平均適合率は、全クラスにおける平均適合率の平均です。これはオブジェクト検出モデルの頑健性を評価し、適合率と再現率のトレードオフを考慮に入れます。", + "Short": "平均適合率", + "Title": "平均適合率スコアに関する追加情報" }, "AveragePrecision": { - "Name": "Average precision score", - "Info": "Average precision for object detection models is calculated for a selected class.", - "Short": "Avg precision", - "Title": "Additional information on average precision score" + "Name": "平均適合率スコア", + "Info": "選択したクラスに対してオブジェクト検出モデルの平均適合率が計算されます。", + "Short": "平均適合率", + "Title": "平均適合率スコアに関する追加情報" }, "AverageRecall": { - "Name": "Average recall score", - "Info": "Average recall for object detection models is calculated for a selected class.", - "Short": "Avg recall", - "Title": "Additional information on average recall score" + "Name": "平均再現率スコア", + "Info": "選択したクラスに対してオブジェクト検出モデルの平均再現率が計算されます。", + "Short": "平均呼び戻し", + "Title": "平均再現率スコアに関する追加情報" }, "metricName": "メトリック名", "metricValue": "メトリック値" }, "MetricSelector": { "selectorLabel": "メトリックを選択します", - "feature1SelectorLabel": "Rows: Feature 1", - "feature2SelectorLabel": "Columns: Feature 2" + "feature1SelectorLabel": "行: 機能 1", + "feature2SelectorLabel": "列: 機能 2" }, "Navigation": { "cohortSaved": "新しいコーホートが保存されました。コーホートの設定でコーホート リストを参照してください。", @@ -433,9 +433,9 @@ "defaultLabelCopy": "すべてのデータ コピー" }, "TreeView": { - "ariaLabel": "Interactive chart", - "disabledArialLabel": "Disabled interactive chart", - "treeMapInfoTitle": "Additional information on tree map", + "ariaLabel": "対話的なチャート", + "disabledArialLabel": "無効な対話型チャート", + "treeMapInfoTitle": "ツリー マップに関する追加情報", "treeDescription": "ツリーの視覚化では、各特徴とエラーの間の相互情報を使用して、エラー インスタンスをデータ内の成功インスタンスから階層的に最適に分離します。これにより、一般的な障害パターンを検出して強調表示するプロセスが簡略化されます。重要な障害パターンを見つけるには、赤色が強く (エラー率が高い) ノードと、より塗りつぶし線の厚い (つまり、エラーカバレッジが高い) ノードを探します。ツリーで使用されている機能のリストを編集するには、[特徴リスト] をクリックします。[メトリックの選択] ドロップダウン メニューを使用して、エラー ノードと成功ノードのパフォーマンスの詳細を確認します。このメトリックの選択により、エラー ツリーの生成方法が影響を受けないことに注意してください。", "treeStaticDescription": "ツリーの視覚化では、各特徴とエラーの間の相互情報を使用して、エラー インスタンスをデータ内の成功インスタンスから階層的に最適に分離します。これにより、一般的な障害パターンを検出して強調表示するプロセスが簡略化されます。重要な障害パターンを見つけるには、赤色が強く (エラー率が高い) ノードと、より塗りつぶし線の厚い (つまり、エラーカバレッジが高い) ノードを探します。このエラー ツリーの作成で使用されている機能のリストを表示するには、[特徴リスト] をクリックします。[メトリックの選択] ドロップダウン メニューを使用して、エラー ノードと成功ノードのパフォーマンスの詳細を確認します。このメトリックの選択により、エラー ツリーの生成方法が影響を受けないことに注意してください。", "disabledWarning": "データセット全体に対してツリーマップが生成されているため、グローバル コーホートが \"すべてのデータ\" を表すように切り替えられる場合を除き、エラー ツリーマップは無効になります。エラー ツリーマップを表示するには、完全なデータセットに切り替えます。" @@ -770,7 +770,7 @@ "countHelperText": "ポイント数のヒストグラム", "ditherLabel": "ディザーが必要", "groupByCohort": "コーホートでグループ化", - "logarithmicScaling": "Enable logarithmic scaling", + "logarithmicScaling": "対数スケーリングを有効にする", "numOfBins": "ビンの数", "selectClass": "クラスを選択", "selectFeature": "特徴量を選択", @@ -794,7 +794,7 @@ "importancePrefix": "重要度", "numberOfDatapoints": "データポイントの数", "rowIndex": "行インデックス", - "absoluteIndex": "Absolute index", + "absoluteIndex": "絶対インデックス", "xValue": "X 値", "yValue": "Y 値" }, @@ -822,12 +822,12 @@ }, "CohortEditor": { "columns": { - "index": "Index", - "dataset": "Dataset", - "predictedY": "Predicted Y", - "trueY": "True Y", - "classificationOutcome": "Classification outcome", - "regressionError": "Error" + "index": "インデックス", + "dataset": "データセット", + "predictedY": "予測された Y", + "trueY": "真の Y", + "classificationOutcome": "分類結果", + "regressionError": "エラー" }, "TreatAsCategorical": "カテゴリ別として扱う", "addFilter": "フィルターの追加", @@ -852,8 +852,8 @@ "save": "保存", "saveAndSwitch": "保存して切り替える", "selectFilter": "フィルターを選択してください", - "noFiltersApplied": "No filters applied", - "filterAdded": "Filter added" + "noFiltersApplied": "適用されたフィルターがありません", + "filterAdded": "フィルターが追加されました" }, "Columns": { "classificationOutcome": "分類結果", @@ -863,8 +863,8 @@ "falsePositive": "擬陽性", "none": "カウント", "predictedProbabilities": "予測確率", - "predictedLabels": "Predicted labels", - "trueLabels": "True labels", + "predictedLabels": "予測ラベル", + "trueLabels": "真のラベル", "regressionError": "回帰エラー", "trueNegative": "真陰性", "truePositive": "真陽性", @@ -885,7 +885,7 @@ "aggregatePlots": "集計のプロット", "chartType": "グラフの種類", "colorValue": "色の値", - "infoTitle": "Additional information on data analysis chart view", + "infoTitle": "データ分析チャート ビューの追加情報", "helperText": "データセット コーホートを作成し、予測される結果、データセットの特徴量、エラー グループなどのフィルターを使ってデータセットの統計を分析します。データセットの提示過剰または提示不足について確認します。", "individualDatapoints": "個々のデータポイント", "missingParameters": "このタブには、評価データセットを指定する必要があります。", @@ -906,6 +906,8 @@ "index": "インデックス", "output": "出力", "predictedY": "予測された Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "確率 : {0}", "trueY": "True Y", "xValue": "X 値:", @@ -974,10 +976,10 @@ "dependencePlotHelperText": "この従属プロットは、その特徴量の重要度の値と対応する特徴量の重要度の関係を示しています。", "dependencePlotTitle": "従属プロット", "helperText": "モデル全体の予測に影響を与える上位 k 個の重要な特徴量を調査することができます (グローバルな説明)。スライダーを使用して、機能の重要度を降順に表示します。すべてのコーホートの特徴量の重要度が横に並んで表示され、凡例でコーホートを選択することにより、表示を切り替えることができます。グラフ内の任意の特徴量をクリックすると、選択した特徴量の値が予測にどのように影響するかを示す密度プロットが下に表示されます。", - "infoTitle": "Additional information on aggregate feature importance", + "infoTitle": "集計特徴量の重要度に関する追加情報", "legendHelpText": "凡例項目をクリックして、プロットのコーホートのオンとオフを切り替えます。", "missingParameters": "このタブには、ローカル特徴量の重要度パラメーターを指定する必要があります。", - "sortByCohort": "Sort by cohort", + "sortByCohort": "コーホートで並べ替え", "sortBy": "データポイントで並べ替え", "topAtoB": "重要度別の上位 {0} 機能", "viewDependencePlotFor": "従属プロットの表示:", @@ -1020,15 +1022,15 @@ }, "Statistics": { "accuracy": "正確性: {0}", - "bleuScore": "Bleu score: {0}", - "bertScore": "Bert score: {0}", - "exactMatchRatio": "Exact match ratio: {0}", - "rougeScore": "Rouge Score: {0}", + "bleuScore": "BLEU スコア: {0}", + "bertScore": "バート スコア: {0}", + "exactMatchRatio": "完全一致率: {0}", + "rougeScore": "ROUGE スコア: {0}", "fnr": "擬陰性の割合: {0}", "fpr": "擬陽性の割合: {0}", - "hammingScore": "Hamming score: {0}", + "hammingScore": "ハミング スコア: {0}", "meanPrediction": "平均予測 {0}", - "meteorScore": "Meteor Score: {0}", + "meteorScore": "METEOR スコア: {0}", "mse": "平均二乗誤差: {0}", "precision": "適合率: {0}", "rSquared": "R²: {0}", @@ -1036,10 +1038,10 @@ "selectionRate": "選択率: {0}", "mae": "平均絶対誤差: {0}", "f1Score": "F1 スコア: {0}", - "samples": "Sample size: {0}", - "meanAveragePrecision": "Mean average precision: {0}", - "averagePrecision": "Average precision: {0}", - "averageRecall": "Average recall: {0}" + "samples": "サンプル サイズ: {0}", + "meanAveragePrecision": "平均適合率: {0}", + "averagePrecision": "平均適合率: {0}", + "averageRecall": "平均再現率: {0}" }, "ValidationErrors": { "addFilters": "フィルターの追加", @@ -1147,30 +1149,30 @@ "InterpretText": { "View": { "interpretibilityDashboard": "解釈可能性ダッシュボード", - "importantWords": "Show most important words", + "importantWords": "最も重要な単語を表示する", "topFeatureList": "上位特徴リストの分析", "allButton": "すべての特徴量", "negButton": "負の特徴", "posButton": "正の特徴量", - "legendText": "Positive scalar feature importances represent the extent that the words were important towards the classification of your selected label, and negative scalar feature importances represent words that encouraged your model away from your selected label.", - "legendTextForQA": "The left text box and the bar chart display the predictions of the model. The right text box shows the feature importance associated with a selected token. Positive feature importances represent the extent that the words were important towards marking the selected token as the starting/ending position of the answer.", + "legendText": "正のスカラー特徴量の重要度は、選択したラベルの分類に対してワードが重要であった範囲を表し、負のスカラー特徴量の重要度は、選択したラベルからモデルを遠ざけたワードを表します。", + "legendTextForQA": "左のテキスト ボックスと横棒グラフには、モデルの予測が表示されます。右側のテキスト ボックスには、選択したトークンに関連付けられている特徴量の重要度が表示されます。正の特徴量の重要度は、選択したトークンを回答の開始位置または終了位置としてマークする上で単語が重要であった程度を表します。", "label": "ラベル", "colon": ": ", - "startingPosition": "STARTING POSITION", - "endingPosition": "ENDING POSITION", - "predictedAnswer": "Predicted answer: ", - "trueAnswer": "True answer: ", - "inputs": "Inputs", - "outputs": "Outputs", - "sliderAriaLabel": "Slider for most important words" + "startingPosition": "開始位置", + "endingPosition": "終了位置", + "predictedAnswer": "予測される回答: ", + "trueAnswer": "実際の回答: ", + "inputs": "入力", + "outputs": "出力", + "sliderAriaLabel": "最も重要な単語のスライダー" }, "Legend": { "featureLegend": "テキスト特徴の凡例", "posFeatureImportance": "正の特徴量の重要度", "negFeatureImportance": "負の特徴量の重要度", - "cls": "CLS: start of the sentence", - "sep": "SEP: end of the sentence", - "selectedWord": "Selected word: " + "cls": "CLS: 文の始まり", + "sep": "SEP: 文の終わり", + "selectedWord": "選択した単語: " }, "BarChart": { "featureImportance": "特徴量の重要度" @@ -1178,59 +1180,59 @@ }, "InterpretVision": { "Cohort": { - "close": "Close", - "errorCohortName": "Please choose a unique cohort name.", - "errorNumSelected": "Please select at least one (1) item.", - "itemsSelectedSingular": "item selected", - "itemsSelectedPlural": "items selected", - "save": "Save cohort", - "saveAndClose": "Save and close", - "saveAndSwitch": "Save and switch", - "textField": "New cohort name", - "title": "Save new cohort" + "close": "閉じる", + "errorCohortName": "一意のコーホート名を選択してください。", + "errorNumSelected": "少なくとも 1 つの項目を選択してください。", + "itemsSelectedSingular": "選択された項目", + "itemsSelectedPlural": "選択された項目", + "save": "コーホートを保存する", + "saveAndClose": "保存して閉じる", + "saveAndSwitch": "保存して切り替える", + "textField": "新しいコーホート名", + "title": "新しいコーホートの作成" }, "Dashboard": { "allData": "すべてのデータ", - "columnOne": "Image", + "columnOne": "画像", "columnTwo": "インデックス", "columnThree": "True Y", "columnFour": "予測された Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "正解", + "columnFourOD": "不正解", "columnFive": "その他のメタデータ", - "chooseObject": "Choose a detected object", - "examples": "examples", + "chooseObject": "検出されたオブジェクトの選択", + "examples": "例", "filter": "フィルター", - "indexLabel": "Image ", - "labelTypeDropdown": "Select label type", - "labelVisibilityDropdown": "Select labels to display", - "legendFailure": "failure", - "legendSuccess": "success", - "loading": "Computing explanation for index", - "multiselect": "Multiselect", - "notdefined": "object scenario not defined", - "objectSelect": "Object Selection", + "indexLabel": "画像 ", + "labelTypeDropdown": "ラベルの種類の選択", + "labelVisibilityDropdown": "表示するラベルの選択", + "legendFailure": "失敗", + "legendSuccess": "成功", + "loading": "インデックスの説明を計算しています", + "multiselect": "複数選択", + "notdefined": "オブジェクト シナリオが定義されていません", + "objectSelect": "オブジェクトの選択", "pageSize": "ページ サイズ: ", - "panelTitle": "Selected instance", - "panelExplanation": "Explanation", - "panelInformation": "Information", - "predictedLabel": "Predicted label", - "predictedY": "Predicted: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", - "prefix": "Object: ", - "rows": "Rows: ", + "panelTitle": "選択したインスタンス", + "panelExplanation": "説明", + "panelInformation": "情報", + "predictedLabel": "予測ラベル", + "predictedY": "予測: ", + "correctDetections": "正しい検出:", + "incorrectDetections": "正しくない検出:", + "prefix": "オブジェクト: ", + "rows": "行: ", "search": "検索", - "selectAll": "Select all", + "selectAll": "すべてを選択", "settings": "設定", - "showAll": "Show all", + "showAll": "すべて表示", "tabOptionFirst": "イメージ エクスプローラー ビュー", "tabOptionSecond": "テーブル ビュー", - "tabOptionThird": "Class view", + "tabOptionThird": "クラス ビュー", "thumbnailSize": "サムネイルのサイズ", "titleBarError": "エラー インスタンス", "titleBarSuccess": "成功インスタンス", - "trueY": "Ground truth: " + "trueY": "グラウンド トゥルース: " } }, "ModelAssessment": { @@ -1239,15 +1241,15 @@ "CalloutContent": "コンポーネント (エラー ツリー ビュー、エラー ヒート マップ) を追加すると、下のコンポーネントに表示されるグローバル コーホートからデータをフィルター処理できるようになります。", "CalloutTitle": "コンポーネントの追加", "TabAddedMessage": { - "DataAnalysis": "Data analysis component added", - "FeatureImportances": "Feature importances component added", - "ErrorAnalysis": "Error analysis component added", - "Fairness": "Fairness component added", - "ModelOverview": "Model overview component added", - "CausalAnalysis": "Causal analysis component added", - "Counterfactuals": "Counterfactuals component added", - "Vision": "Vision data explorer component added", - "Forecasting": "Forecasting what-if component added" + "DataAnalysis": "データ分析コンポーネントが追加されました", + "FeatureImportances": "特徴量の重要度コンポーネントが追加されました", + "ErrorAnalysis": "エラー分析コンポーネントが追加されました", + "Fairness": "公平性コンポーネントが追加されました", + "ModelOverview": "モデルの概要コンポーネントが追加されました", + "CausalAnalysis": "原因分析コンポーネントが追加されました", + "Counterfactuals": "反ファクト コンポーネントが追加されました", + "Vision": "Vision データ エクスプローラー コンポーネントが追加されました", + "Forecasting": "What-if コンポーネントが追加されたかどうかを予測しています" } }, "CausalAnalysis": { @@ -1275,7 +1277,7 @@ }, "CohortInformation": { "ShiftCohort": "コーホートの切り替え", - "SwitchTimeSeries": "Switch time series", + "SwitchTimeSeries": "時系列の切り替え", "NewCohort": "新しいコーホート", "DataPoints": "データポイントの数", "DefaultCohort": " (既定)", @@ -1287,7 +1289,7 @@ "CohortSettingsTitle": "コーホートの設定" }, "ComponentNames": { - "ChartView": "Chart view", + "ChartView": "チャート ビュー", "CausalAnalysis": "原因分析", "Counterfactuals": "反事実", "DataAnalysis": "データ分析", @@ -1296,10 +1298,10 @@ "ErrorAnalysis": "エラー分析", "Fairness": "公平性", "FeatureImportances": "特徴量の重要度", - "Forecasting": "Forecasting", + "Forecasting": "予測", "ModelOverview": "モデルの概要", - "TableView": "Table view", - "VisionTab": "Vision data explorer" + "TableView": "テーブル ビュー", + "VisionTab": "データ エクスプローラーを開く" }, "DashboardSettings": { "Content": "このリストには、ダッシュボードのレイアウトが表示されます。エラー分析コンポーネントを使用してデータをフィルター処理し、以下のコンポーネントで表示できます。", @@ -1458,16 +1460,16 @@ "GlobalExplanation": "特徴量の重要度集約", "IncorrectPredictions": "正しくない予測", "InfoTitle": "Additional information on feature importance values", - "IndividualFeatureTabular": "Select a datapoint by clicking on a datapoint (up to 5 datapoints) in the table to view their local feature importance values (local explanation) and individual conditional expectation (ICE) plots.", + "IndividualFeatureTabular": "テーブル内のデータポイント (最大 5 つのデータポイント) をクリックし、そのローカル特徴量の重要度の値 (ローカルの説明) と個々の条件付き期待値 (ICE) プロットを以下に表示します。", "IndividualFeatureText": "Select a datapoint by clicking on a datapoint in the table to view the local feature importance values (local explanation) from SHAP's text explainer.", "LocalExplanation": "個々の特徴量の重要度", "SelectionCounter": "{0}/{1} データポイントが選択されました", "SelectionLimit": "現時点では最大 5 つのデータポイントを選択できます。", - "RowCheckboxAriaLabel": "Row checkbox", - "SelectionColumnAriaLabel": "Toggle selection" + "RowCheckboxAriaLabel": "行のチェックボックス", + "SelectionColumnAriaLabel": "選択の切り替え" }, "IndividualFeatureImportanceView": { - "SmallInstanceSelection": "Instance selection" + "SmallInstanceSelection": "インスタンスの選択" }, "MainMenu": { "DashboardSettings": "ダッシュボードの構成", @@ -1483,44 +1485,44 @@ "ModelOverview": { "metrics": { "accuracy": { - "name": "Accuracy score", + "name": "正確性スコア", "description": "正しく分類されたデータ ポイントの割合。" }, "exactMatchRatio": { - "name": "Exact match ratio", - "description": "The ratio of instances classified correctly for every label." + "name": "完全一致率", + "description": "ラベルごとに正しく分類されたインスタンスの比率。" }, "meteorScore": { - "name": "Meteor Score", - "description": "METEOR Score is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision in question answering task." + "name": "METEOR スコア", + "description": "METEOR スコアは、適合率と再現率の調和平均に基づいて計算され、質問回答タスクでは再現率が適合率よりも重視されます。" }, "bleuScore": { - "name": "Bleu Score", - "description": "Bleu Score measures the ratio of words (and/or n-grams) in the machine generated text that appeared in the reference text in question answering task." + "name": "BLEU スコア", + "description": "BLEU スコアは、マシンが生成したテキスト内の単語（および/またはn-gram）のうち、参照テキストにも現れるものの比率を測定します。これは質問回答タスクにおいて用いられます。" }, "bertScore": { - "name": "Bert Score", - "description": "BERTScore focuses on computing semantic similarity between tokens of reference and machine generated text in question answering task." + "name": "バート スコア", + "description": "BERTScore は、参照テキストのトークンと質問応答タスク内の自動生成されたテキストの間の意味的な類似性の計算に焦点を当てています。" }, "rougeScore": { - "name": "Rouge Score", - "description": "Rouge Score measures the ratio of words (and/or n-grams) in the reference text that appeared in the machine generated text in question answering task." + "name": "ROUGE スコア", + "description": "ROUGE スコアは、参照テキスト内の単語（および/またはn-gram）のうち、マシンが生成したテキストにも現れるものの比率を測定します。これは質問回答タスクにおいて用いられます。" }, "hammingScore": { - "name": "Hamming score", - "description": "The average ratio of labels classified correctly among those classified as 1 in multilabel task." + "name": "ハミング スコア", + "description": "マルチラベルタスクにおいて、1 として分類されたラベルの中で正確に分類されたラベルの平均比率。" }, "f1Score": { "name": "F1 スコア", "description": "F1 スコアは適合率と再現率の調和平均です。" }, "f1ScoreMacro": { - "name": "Macro F1 score", - "description": "Macro F1 score is the harmonic mean of precision and recall for each class, with each class weighted equally." + "name": "マクロ F1 スコア", + "description": "Macro F1 score は、各クラスが等しく重み付けされた、各クラスの適合率と再現率の調和平均です。" }, "f1ScoreMicro": { - "name": "Micro F1 score", - "description": "Micro F1 score is the harmonic mean of precision and recall for each class, with each class weighted according to how many instances it contains." + "name": "マイクロ F1 スコア", + "description": "Micro F1 スコアは、各クラスの適合率と再現率の調和平均であり、それぞれのクラスはその中に含まれるインスタンスの数に比例して重み付けされます。" }, "meanAbsoluteError": { "name": "平均絶対誤差", @@ -1535,24 +1537,24 @@ "description": "1 に分類されたデータ ポイントの中で正しく分類されたデータ ポイントの割合。" }, "precisionMacro": { - "name": "Macro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted equally." + "name": "マクロ適合率スコア", + "description": "各クラスにおいて、1 と分類されたデータポイントの中で正しく分類されたものの割合。ここで、全てのクラスは等しく重み付けされます。" }, "precisionMicro": { - "name": "Micro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted according to how many instances it contains." + "name": "マイクロ適合率スコア", + "description": "1として分類された各クラスのデータポイントの中で、それぞれのクラスが含むインスタンスの数に比例して重み付けされ、正しく分類されたデータポイントの割合。" }, "recall": { "name": "再現率スコア", "description": "true ラベルが 1 のデータ ポイントの中で正しく分類されたデータ ポイントの割合。代替名: 真陽性率、感度。" }, "recallMacro": { - "name": "Macro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted equally." + "name": "マクロ再現率スコア", + "description": "各クラスにおいて、真のラベルが 1 であるデータポイントの中で正しく分類されたものの割合。ここで、全てのクラスは等しく重み付けされます。" }, "recallMicro": { - "name": "Micro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted according to how many instances it contains." + "name": "マイクロ再現率スコア", + "description": "それぞれのクラスが含むインスタンスの数に比例して重み付けされた各クラスで、真のラベルが 1 であるデータポイントの中で正しく分類されたものの割合。" }, "falsePositiveRate": { "name": "擬陽性の割合", @@ -1571,32 +1573,32 @@ "description": "すべての予測の平均。" }, "meanAveragePrecision": { - "name": "Mean Average Precision score", - "description": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall." + "name": "平均適合率スコア", + "description": "オブジェクト検出モデルにおける平均適合率は、全クラスにおける平均適合率の平均です。これはオブジェクト検出モデルの頑健性を評価し、適合率と再現率のトレードオフを考慮に入れます。" }, "averagePrecision": { - "name": "Average Precision score", - "description": "Average precision for object detection models is calculated for a selected class." + "name": "平均適合率スコア", + "description": "選択したクラスに対してオブジェクト検出モデルの平均適合率が計算されます。" }, "averageRecall": { - "name": "Average Recall score", - "description": "Average recall for object detection models is calculated for a selected class." + "name": "平均再現率スコア", + "description": "選択したクラスに対してオブジェクト検出モデルの平均再現率が計算されます。" }, "fairnessMetricDifference": "差分", "fairnessMetricRatio": "率" }, "metricsDropdown": "メトリック", - "metricsTypeDropdown": "Aggregate method", + "metricsTypeDropdown": "集計メソッド", "metricTypes": { - "macro": "Macro", - "micro": "Micro" + "macro": "マクロ", + "micro": "マイクロ" }, - "classSelectionDropdown": "Select class(es)", + "classSelectionDropdown": "クラスを選択", "iouThresholdDropdown": { - "name": "IoU Threshold", - "description": "Intersection over Union quantifies the degree of overlap between the prediction and ground truth bounding box of a detected object in an image. For example, setting an IoU threshold of 70% means that a prediction with greater than 70% overlap with ground truth is True, thus influencing the definition of prediction correctness and calculation of other performance metrics.", + "name": "IoU のしきい値:", + "description": "IoU は、画像内で検出されたオブジェクトの予測とグラウンド トゥルースの境界ボックス間の重複の度合いを示します。たとえば、IoU のしきい値を 70% に設定すると、70% を超える予測がグラウンド トゥルースと重なっていることを意味し、予測の正確性の定義と他のパフォーマンス メトリックの計算に影響します。", "iconId": "iouThresholdIconId", - "title": "Learn about the IoU threshold" + "title": "IoU のしきい値の詳細" }, "notAvailable": "該当なし", "countColumnHeader": "サンプル サイズ", @@ -1608,14 +1610,14 @@ "featuresDropdown": "特徴", "metricChartDropdownSelectionHeader": "メトリック", "probabilityForClassSelectionHeader": "クラスの確率", - "targetSelectionHeader": "Target", + "targetSelectionHeader": "対象", "metricSelectionDropdownPlaceholder": "コーホートを比較するメトリックを選択します。", - "classSelectionDropdownPlaceholder": "Select class name for class-based analysis.", + "classSelectionDropdownPlaceholder": "クラス ベースの分析のクラス名を選択します。", "featureSelectionDropdownPlaceholder": "特徴ベースの分析に使用する特徴を選択します。", "probabilityDistributionPivotItem": "確率分布", - "regressionDistributionPivotItem": "Target distribution", + "regressionDistributionPivotItem": "ターゲットの分布", "metricsVisualizationsPivotItem": "メトリックの視覚化", - "confusionMatrixPivotItem": "Confusion matrix", + "confusionMatrixPivotItem": "混同行列", "disaggregatedAnalysisFeatureSelectionPlaceholder": "特徴ベースの分析を生成する特徴を選択します。", "tableCountTooltip": "コーホート {0} には {1} インスタンスが含まれています。", "tableMetricTooltip": "コーホート {1} に対するモデルの {0} が {2}", @@ -1626,36 +1628,36 @@ "metricSelectionButton": "メトリックの選択", "cohortSelectionButton": "コーホートの選択", "probabilityLabelSelectionButton": "ラベルの選択", - "regressionTargetSelectionButton": "Choose target", + "regressionTargetSelectionButton": "ターゲットの選択", "selectAllCohortsOption": "すべてを選択", "other": "その他", "BoxPlot": { "outlierProbability": "確率", "outlierLabel": "外れ値", "boxPlotSeriesLabel": "ボックス プロット", - "lowerWhisker": "Lower whisker", - "upperWhisker": "Upper whisker", - "median": "Median", - "lowerQuartile": "Lower quartile", - "upperQuartile": "Upper quartile" + "lowerWhisker": "下ひげ", + "upperWhisker": "上ひげ", + "median": "中央値", + "lowerQuartile": "下位四分位数", + "upperQuartile": "上位四分位数" }, "chartConfigApply": "適用", "chartConfigCancel": "キャンセル", "chartConfigDatasetCohortSelectionPlaceholder": "データセット コーホートの選択", "chartConfigFeatureBasedCohortSelectionPlaceholder": "特徴ベースのコーホートの選択", "confusionMatrix": { - "confusionMatrixCohortSelectionLabel": "Select dataset cohort", - "confusionMatrixClassSelectionLabel": "Select classes", - "confusionMatrixClassMinSelectionError": "Select at least {0} classes to visualize the confusion matrix.", - "confusionMatrixClassMaxSelectionError": "Select at most {0} classes to visualize the confusion matrix.", - "confusionMatrixClassSelectionDefaultPlaceholder": "Choose classes", - "confusionMatrixHeatmapTooltip": "{0} datapoints should be {1}, predicted to be {2}", - "confusionMatrixYAxisLabel": "True Class", - "confusionMatrixXAxisLabel": "Predicted Class", - "class": "Class" + "confusionMatrixCohortSelectionLabel": "データセット コーホートの選択", + "confusionMatrixClassSelectionLabel": "クラスの選択", + "confusionMatrixClassMinSelectionError": "少なくとも {0} のクラスを選択して、混同マトリックスを視覚化します。", + "confusionMatrixClassMaxSelectionError": "最大 {0} のクラスを選択して、混同マトリックスを視覚化します。", + "confusionMatrixClassSelectionDefaultPlaceholder": "クラスの選択", + "confusionMatrixHeatmapTooltip": "{0}データポイントは{1}する必要があり、{2}であると予測されます", + "confusionMatrixYAxisLabel": "真のクラス", + "confusionMatrixXAxisLabel": "予測されたクラス", + "class": "クラス" }, "nA": "該当なし", - "disaggregatedAnalysisBaseCohortDisclaimer": "The cohorts in the following feature-based analysis are based on the global cohort, {0}.", + "disaggregatedAnalysisBaseCohortDisclaimer": "次の特徴ベースの分析のコーホートは、グローバル コーホート {0} に基づいています。", "disaggregatedAnalysisBaseCohortWarning": "{0} コーホートとは異なり、{1} にはフィルターが含まれています。結果として、データセット全体のサブセットのみがキャプチャされ、分析情報は完全なデータセットに一般化されない可能性があります。", "probabilitySplineChartToggleLabel": "スプライン グラフを使用する", "countAxisLabel": "カウント", @@ -1685,76 +1687,76 @@ "flyoutDescription": "データセット コーホートまたは機能コーホートを表示するように選択できます。機能コーホートを使用できない場合は、まず機能コーホート ビューで 1 つ以上の機能を選択する必要があります。その後、機能コーホートが生成され、ここで選択できます。" }, "regressionTargetOptions": { - "predictedY": "Predicted Y", - "trueY": "True Y", - "error": "Error" + "predictedY": "予測された Y", + "trueY": "真の Y", + "error": "エラー" }, "topLevelDescription": "予測値の分布とモデルのパフォーマンス メトリックの値を調べて、モデルのパフォーマンスを評価します。[データセット コーホート] タブを使用して、さまざまな事前構築済みまたは新しく作成されたデータセット コーホート間のパフォーマンスの比較分析を調べて、モデルを調査します。\"特徴コーホート\" を使用して、センシティブ特徴サブコーホート間のパフォーマンスの比較分析を調べて、モデルを調査します。(たとえば、異なる性別間のパフォーマンス、収入レベルなど)。", - "infoTitle": "Additional information on model overview", + "infoTitle": "モデルの概要に関する追加情報", "visualDisplayToggleLabel": "ヒートマップの表示", "featureBasedViewDescription": "最大 2 つの特徴を選択すると、特徴ベースのコーホート (1 つの特徴が選択されている場合) または交差コーホート (2 つの特徴が選択されている場合) のモデル パフォーマンスの内訳が表示されます。" }, "TableViewTab": { - "Heading": "View the dataset in a table format for all features and rows." + "Heading": "すべての機能と行のデータセットをテーブル形式で表示します。" } }, "Forecasting": { - "target": "Target", - "whatIfForecastingHeader": "What-if analysis", - "forecastHeader": "Forecast analysis", - "whatIfForecastingDescription": "What-if allows you to perturb features for your entire time series and observe how the model's forecast changes.", - "whatIfForecastingChooseTimeSeries": "To start, choose a time series from the options below.", - "forecastDescription": "Forecast analysis compares your model's forecast to the actual values of your time series. To enable what-if analysis, provide a dataset with features.", - "timeSeries": "Time series", - "selectTimeSeries": "Select a time series.", - "singleTimeSeries": "The dataset contains only a single time series '{0}' which has been selected by default.", - "trueY": "True Y", - "baselinePrediction": "Baseline prediction", - "forecastComparisonHeader": "Compare What-if Forecasts", - "forecastComparisonChartTitle": "Forecasts", - "forecastComparisonChartTimeAxisLabel": "Time", + "target": "対象", + "whatIfForecastingHeader": "What-if 分析", + "forecastHeader": "予測分析", + "whatIfForecastingDescription": "What-if を使用すると、時系列全体の機能を摂動し、モデルの予測がどのように変化するかを監視できます。", + "whatIfForecastingChooseTimeSeries": "開始するには、以下のオプションから時系列を選択します。", + "forecastDescription": "予測分析では、モデルの予測と時系列の実際の値が比較されます。What-if 分析を有効にするには、機能を含むデータセットを指定します。", + "timeSeries": "時系列", + "selectTimeSeries": "タイム シリーズの選択。", + "singleTimeSeries": "データセットには、既定で選択されている 1 つの時系列'{0}'のみが含まれています。", + "trueY": "真の Y", + "baselinePrediction": "ベースライン予測", + "forecastComparisonHeader": "What-if 予測の比較", + "forecastComparisonChartTitle": "予測", + "forecastComparisonChartTimeAxisLabel": "時間", "Transformations": { - "multiply": "multiply", - "divide": "divide", - "add": "add", - "subtract": "subtract", - "change": "change to" + "multiply": "乗算", + "divide": "除算", + "add": "加算", + "subtract": "減算", + "change": "次へ変更:" }, "TransformationCreation": { - "title": "Create what-if scenario", - "nameLabel": "What-if scenario name", - "featureInstructions": "Choose a feature to perturb.", - "operationInstructions": "Choose an operation to apply to the feature.", - "operationDropdownHeader": "Operation", - "featureDropdownHeader": "Feature", - "valueSpinButtonHeader": "Value", - "scenarioNamingInstructionsPlaceholder": "Enter a unique name", - "scenarioNamingInstructions": "Enter a name for your what-if scenario.", - "scenarioNamingCollisionMessage": "This name exists already. Please enter a unique name.", - "scenarioNamingLengthMessage": "The name must be between 1 and 50 characters. The actual length is {0}.", - "scenarioNamingInvalidCharactersMessage": "The name can only contain alphanumeric characters, whitespaces, dashes, or underscores, and needs to start with an alphanumeric character.", - "valueErrorMessage": "For operation {0} please select a value other than {1}.", - "invalidCombinationErrorMessage": "This is identical to an existing what-if scenario. Please change the feature, operation, or value.", - "addTransformationButton": "Add Transformation", - "divisionAndMultiplicationBy": "by" + "title": "What-if シナリオの作成", + "nameLabel": "What-if シナリオ名", + "featureInstructions": "摂動する機能を選択します。", + "operationInstructions": "機能に適用する操作を選択します。", + "operationDropdownHeader": "操作", + "featureDropdownHeader": "特徴量", + "valueSpinButtonHeader": "値", + "scenarioNamingInstructionsPlaceholder": "一意の名前を入力してください", + "scenarioNamingInstructions": "What-if シナリオの名前を入力します。", + "scenarioNamingCollisionMessage": "この名前は既に存在します。一意の名前を入力してください。", + "scenarioNamingLengthMessage": "名前は 1 から 50 文字にする必要があります。実際の長さは{0}です。", + "scenarioNamingInvalidCharactersMessage": "名前に使用できるのは、英数字、空白、ダッシュ、アンダースコアのみです。先頭には英数字を使用する必要があります。", + "valueErrorMessage": "操作{0}の場合は、{1}以外の値を選択してください。", + "invalidCombinationErrorMessage": "これは、既存の what-if シナリオと同じです。機能、操作、または値を変更してください。", + "addTransformationButton": "変換の追加", + "divisionAndMultiplicationBy": "条件" }, "TransformationTable": { - "nameColumnHeader": "Name", - "methodColumnHeader": "Method", - "divisionAndMultiplicationBy": "by ", - "header": "What-if Forecasts ({0})" + "nameColumnHeader": "名前", + "methodColumnHeader": "メソッド", + "divisionAndMultiplicationBy": "条件 ", + "header": "What-if 予測 ({0})" }, "TimeSeries": { - "apply": "Apply", - "cancel": "Cancel", - "cohortList": "Time series list", - "selectCohort": "Select a time series", - "shiftCohort": "Switch time series", - "shiftCohortDescription": "Select a time series from the time series list. Apply the time series to the dashboard." + "apply": "適用", + "cancel": "キャンセル", + "cohortList": "時系列リスト", + "selectCohort": "タイム シリーズの選択", + "shiftCohort": "時系列の切り替え", + "shiftCohortDescription": "時系列リストから時系列を選択します。時系列をダッシュボードに適用します。" }, "TimeSeriesSettings": { - "CohortSettingsDescription": "Time series are pre-defined based on time series identifying columns.", - "CohortSettingsTitle": "Time series settings" + "CohortSettingsDescription": "時系列は、列を識別する時系列に基づいて事前に定義されています。", + "CohortSettingsTitle": "時系列の設定" } } } \ No newline at end of file diff --git a/libs/localization/src/lib/en.ko.json b/libs/localization/src/lib/en.ko.json index 8164a3024c..08ee98373f 100644 --- a/libs/localization/src/lib/en.ko.json +++ b/libs/localization/src/lib/en.ko.json @@ -906,6 +906,8 @@ "index": "인덱스", "output": "출력", "predictedY": "예측된 Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "확률: {0}", "trueY": "True Y", "xValue": "X 값:", @@ -1147,7 +1149,7 @@ "InterpretText": { "View": { "interpretibilityDashboard": "해석 가능성 대시보드", - "importantWords": "Show most important words", + "importantWords": "가장 중요한 단어 표시", "topFeatureList": "상위 기능 목록 분석", "allButton": "모든 기능", "negButton": "부정적인 기능", @@ -1162,7 +1164,7 @@ "trueAnswer": "실제 답변: ", "inputs": "입력", "outputs": "출력", - "sliderAriaLabel": "Slider for most important words" + "sliderAriaLabel": "가장 중요한 단어의 슬라이더" }, "Legend": { "featureLegend": "텍스트 기능 범례", @@ -1195,8 +1197,8 @@ "columnTwo": "지수", "columnThree": "True Y", "columnFour": "예측 Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "맞음", + "columnFourOD": "잘못됨", "columnFive": "기타 메타데이터", "chooseObject": "검색된 개체 선택", "examples": "예제", @@ -1216,8 +1218,8 @@ "panelInformation": "정보", "predictedLabel": "예측 레이블", "predictedY": "예측: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", + "correctDetections": "올바른 탐지: ", + "incorrectDetections": "잘못된 탐지: ", "prefix": "개체: ", "rows": "행: ", "search": "검색", diff --git a/libs/localization/src/lib/en.nl.json b/libs/localization/src/lib/en.nl.json index 4d197cf14e..3abdceedf8 100644 --- a/libs/localization/src/lib/en.nl.json +++ b/libs/localization/src/lib/en.nl.json @@ -3,26 +3,26 @@ "close": "Sluiten", "tooltipButton": "Knop Knopinfo", "identityFeature": "Identiteitsfunctie", - "infoTitle": "Additional information", - "spinButton": "Spin", - "editButton": "Edit", - "decreaseValue": "Decrease value", - "increaseValue": "Increase value", - "decreaseValueByOne": "Decrease value by 1", - "increaseValueByOne": "Increase value by 1", - "loading": "Loading..." + "infoTitle": "Aanvullende informatie", + "spinButton": "Draaien", + "editButton": "Bewerken", + "decreaseValue": "Waarde verlagen", + "increaseValue": "Waarde verhogen", + "decreaseValueByOne": "Waarde met 1 verlagen", + "increaseValueByOne": "Waarde met 1 verhogen", + "loading": "Laden..." }, "ChartContextMenu": { - "hideData": "Hide data table", - "viewData": "View data table", - "viewInFullScreen": "View in full screen", - "printChart": "Print chart", - "downloadCSV": "Download CSV", - "downloadPNG": "Download PNG image", - "downloadJPEG": "Download JPEG image", - "downloadPDF": "Download PDF document", - "downloadSVG": "Download SVG vector image", - "downloadXLS": "Download XLS" + "hideData": "Gegevenstabel verbergen", + "viewData": "Gegevenstabel weergeven", + "viewInFullScreen": "Weergeven in volledig scherm", + "printChart": "Grafiek afdrukken", + "downloadCSV": "CSV downloaden", + "downloadPNG": "PNG-afbeelding downloaden", + "downloadJPEG": "JPEG-afbeelding downloaden", + "downloadPDF": "PDF-document downloaden", + "downloadSVG": "SVG-vectorafbeelding downloaden", + "downloadXLS": "XLS downloaden" }, "CausalAnalysis": { "AggregateView": { @@ -39,7 +39,7 @@ "description": "De oorzakelijke analyse beantwoordt 'Wat als'-vragen over de manier waarop reële resultaten zouden zijn gewijzigd als iemand zich anders zou hebben gedragen, zoals het vervolgen van een andere prijsstrategie voor een product of een alternatieve behandeling voor een patiënt. In tegenstelling tot modelvoorspellingen die belangrijke correlatiepatronen identificeren, helpen deze hulpprogramma's u de belangrijkste oorzakelijke functies te identificeren die direct van invloed zijn op het resultaat van belang. Met deze modellen wordt het oorzakelijke effect van één kenmerk bepaald (ofwel 'behandeling'), waarbij andere variabele kenmerken constant zijn. Voor de beste resultaten moet u ervoor zorgen dat u de volledige gegevensset alle beschikbare kenmerken bevat die kunnen correleren met het resultaat van de variabelen.", "directAggregate": "Directe aggregatie van het oorzakelijk effect van elke behandeling met een betrouwbaarheidsinterval van 95%", "here": "hier", - "infoTitle": "Additional information on aggregated causal effects", + "infoTitle": "Aanvullende informatie over geaggregeerde oorzakelijke effecten", "lasso": "Een lasso (of logistieke regressie als y binair is) was geschikt om y van X[-i] te voorspellen en een lasso (of logistieke regressie als X[i] categorisch is) was geschikt om X[i] te voorspellen vanuit Χ [-i]. Het oorzakelijke effect kan worden weergegeven als de gemiddelde correlatie van de residuen/verrassingsvariatie van de twee voorspellingstaken. Meer informatie over Double Machine Learning", "unconfounding": "Wat zijn verwarrende kenmerken?" }, @@ -51,7 +51,7 @@ "description": "Individuele oorzakelijke effecten kunnen gepersonaliseerde tussenkomsten inlichten, zoals een gerichte promotie naar klanten of een geïndividualiseerd behandelplan. Hoe zou een persoon met een bepaalde set kenmerken reageren op een wijziging in een oorzakelijke functie of behandeling? Het oorzakelijke wat-als-hulpprogramma berekent marginale veranderingen in de werkelijke resultaten voor een bepaalde persoon als u het niveau van een behandeling wijzigt. Met deze analyse kunt u begrijpen hoe de werkelijke resultaten zouden zijn gewijzigd onder verschillende beleidskeuzes, zoals een andere prijsstrategie voor een product of een alternatieve behandeling voor een patiënt. Geef de behandeling van interesse op en kijk hoe het werkelijke resultaat zou veranderen.", "directIndividual": "Direct individueel oorzakelijk effect van elke behandeling met een betrouwbaarheidsinterval van 95%", "index": "Gegevenspunt-index", - "infoTitle": "Additional information on individual causal what-if", + "infoTitle": "Aanvullende informatie over individuele oorzakelijke wat-als", "missingParameters": "Op dit tabblad moet een evaluatiegegevensset worden opgegeven.", "newOutcome": "Nieuw resultaat", "selectTreatment": "Behandeling selecteren", @@ -85,7 +85,7 @@ "averageGainBinary": "Gemiddelde winst van het instellen van de behandeling {0} naar de basislijnwaarde {1}.", "averageGainContinuous": "Gemiddelde winst van alternatief beleid ten opzichte van geen '{0}' behandeling.", "header": "Deze hulpprogramma's helpen bij het bouwen van beleid voor toekomstige interventies. U kunt bepalen welke delen van uw steekproef het meeste antwoord geven op wijzigingen in oorzakelijke kenmerken of behandelingen, en regels opstellen om te definiëren welke toekomstige populaties moeten worden gericht op bepaalde interventies.", - "infoTitle": "Additional information on treatment policy", + "infoTitle": "Aanvullende informatie over het behandelingsbeleid", "nSample": "n = {0}", "noData": "Geen gegevens" } @@ -116,8 +116,8 @@ "cancel": "Annuleren", "title": "Cohort wisselen", "subText": "Selecteer een cohort in de lijst met cohorten. Pas het cohort toe op het dashboard.", - "selectCohort": "Select a cohort", - "cohortList": "Cohort list" + "selectCohort": "Een cohort selecteren", + "cohortList": "Cohortlijst" }, "PreBuiltCohort": { "featureNameNotFound": "Kan de functienaam niet vinden in de gegevensset", @@ -148,13 +148,13 @@ "predictedClass": "Voorspelde klasse", "predictedValue": "Voorspelde waarde" }, - "Size": "Size", - "loading": "Loading...", + "Size": "Grootte", + "loading": "Laden...", "counterfactualEx": "Counterfactual Ex {0}", "counterfactualName": "Naam wat-als-nulscenario's", "createWhatIfCounterfactual": "Wat-als-counterfactual maken", "createCounterfactual": "Nulscenario", - "revertToBubbleChart": "View bubble chart", + "revertToBubbleChart": "Bellendiagram weergeven", "createOwn": "Uw eigen nulscenario maken:", "currentClass": "Huidige klasse", "currentRange": "Huidig bereik", @@ -167,9 +167,9 @@ "listDescription": "In deze lijst ziet u welke gegevenspunten in het huidige gegevensvoorbeeld het meeste oorzakelijke antwoord op de geselecteerde behandeling hebben, op basis van alle behandelingen die zijn opgenomen in het geschatte oorzakelijke model. In de linker vijf kolommen wordt aangegeven of de behandeling wordt aanbevolen voor de observatie, de huidige behandeling, het geschatte effect van de behandeling (effect van het toepassen van een behandeling vanaf een basislijn van geen behandeling voor binaire behandelingen of het vergroten/verkleinen van de behandelingsfunctie met 10% van de normale behandelgrootte in het voorbeeld: [dynamisch: de numerieke wijziging in de behandeling die gebruikt is] rapporteren), en de lage en hogere betrouwbaarheidsintervallen (CI) voor dit effect. In de resterende kolommen worden de huidige behandelingsstatus en andere kenmerken van elke observatie weergegeven.", "localImportanceDescription": "De belangrijkste functies in rij {0} die moeten verstoord worden om de gewenste modelvoorspelling te bereiken. Op basis van wat-als-analyse voor voorspelling: {1}", "localImportanceSelectData": "Selecteer een gegevenspunt om de lokale belangrijkheidsgrafiek te bekijken", - "largeLocalImportanceSelectData": "Select a bubble, followed by a data point to view local importance chart", - "localImportanceFetchError": "There was an error while fetching the local importance data. Error details: {0} Please check the data used.", - "BubbleChartFetchError": "There was an error while fetching the data. Error details: {0} Please check the data used.", + "largeLocalImportanceSelectData": "Selecteer een bel, gevolgd door een gegevenspunt om het lokale urgentiediagram weer te geven", + "localImportanceFetchError": "Er is een fout opgetreden bij het ophalen van de lokale urgentiegegevens. Foutdetails: {0} Controleer de gebruikte gegevens.", + "BubbleChartFetchError": "Er is een fout opgetreden bij het ophalen van de gegevens. Foutdetails: {0} Controleer de gebruikte gegevens.", "noData": "Geen gegevens", "noFeatures": "Er zijn geen functies beschikbaar", "panelDescription": "Blader door nulscenario's en maak er zelf een. Zoek door functies om voorgestelde waarden uit een verscheidenheid aan voorbeelden van nulscenario's weer te geven. Stel voorgestelde functiewaarden voor nulscenario's in door onder elk nulscenario op de tekst 'waarden instellen' te klikken. Benoem uw nulscenario en sla deze op.", @@ -223,13 +223,13 @@ "subText": "Meer informatie over de geselecteerde cohort. Bewerk de cohortnaam. Verwijder deze cohort." }, "FeatureList": { - "featureList": "Feature List", + "featureList": "Functielijst", "apply": "Toepassen", "features": "Functies", "importances": "Urgenties", "treeMapDescription": "Als u de structuurkaart opnieuw wilt trainen, selecteert u de onderstaande functies en slaat u deze op. De functie-urgenties zijn berekend met behulp van de gemeenschappelijke informatie met de fout in de labels voor true. Gebruik deze als een richtlijn voor het trainen van de structuurkaart.", "staticTreeMapDescription": "Bekijk de functies die zijn gebruikt om de structuurkaart te trainen. De functie-urgenties zijn berekend aan de hand van wederzijdse informatie met de fout op de werkelijke labels.", - "searchResultMessage": "Results displayed out of {resultLength} for {searchValue}" + "searchResultMessage": "Resultaten weergegeven van {resultLength} voor {searchValue}" }, "TreeViewParameters": { "maximumDepth": "Maximale diepte", @@ -295,7 +295,7 @@ "disabledWarning": "De fout-heatmap is uitgeschakeld, tenzij het globale cohort wordt omgeschakeld naar 'Alle gegevens' omdat de heatmap wordt gegenereerd voor de volledige gegevensset. Schakel terug naar de volledige gegevensset om de fout-heatmap te bekijken." }, "MatrixSummary": { - "heatMapInfoTitle": "Additional information on heat map", + "heatMapInfoTitle": "Aanvullende informatie over heatmap", "heatMapDescription": "Met de heatmap kunt u zich richten op specifieke intersectionele functiefilters en geaggregeerde foutpercentages berekenen. Begin met twee gegevenssetfuncties om te vergelijken.", "heatMapStaticDescription": "Met de heatmap kunt u zich richten op specifieke intersectionele functiefilters en geaggregeerde foutpercentages berekenen. Er moeten maximaal twee functies worden geselecteerd om een heatmap te maken via SDK voordat u het dashboard weergeeft." }, @@ -311,108 +311,108 @@ }, "Metrics": { "AccuracyScore": { - "Name": "Accuracy score", - "Info": "The accuracy score represents the ratio of correct to total instances in the data.", - "Short": "Accuracy", - "Title": "Additional information on accuracy score" + "Name": "Nauwkeurigheidsscore", + "Info": "De nauwkeurigheidsscore vertegenwoordigt de verhouding tussen de juiste en het totale aantal exemplaren in de gegevens.", + "Short": "Nauwkeurigheid", + "Title": "Aanvullende informatie over nauwkeurigheidsscore" }, "ErrorRate": { - "Name": "Error rate", - "Info": "The error rate represents the percentage of instances in the node for which the system has failed.", - "Short": "Error rate", - "Title": "Additional information on error rate" + "Name": "Aantal fouten", + "Info": "Met het foutpercentage wordt het percentage exemplaren in het knooppunt aangegeven waarvoor een systeemfout optreedt.", + "Short": "Aantal fouten", + "Title": "Aanvullende informatie over het foutpercentage" }, "F1Score": { - "Name": "F1 score", - "Info": "The F1 score is the harmonic mean of the precision and recall metrics.", - "Short": "F1 score", - "Title": "Additional information on F1 score" + "Name": "F1-score", + "Info": "De F1-score is het metrische gemiddelde van de meetwaarden voor precisie en relevante metrische gegevens.", + "Short": "F1-score", + "Title": "Aanvullende informatie over F1-score" }, "MeanAbsoluteError": { - "Name": "Mean absolute error", - "Info": "The mean absolute error is the average of the sum of the errors.", - "Short": "Mean abs. error", - "Title": "Additional information on mean absolute error" + "Name": "Gemiddelde absolute fout", + "Info": "De gemiddelde absolute fout is het gemiddelde van de som van de fouten.", + "Short": "Gemiddelde abs. fout", + "Title": "Aanvullende informatie over gemiddelde absolute fout" }, "MeanSquaredError": { - "Name": "Mean squared error", - "Info": "The mean squared error is the average of the squares of the errors.", - "Short": "Mean sq. error", - "Title": "Additional information on mean squared error" + "Name": "Gemiddelde kwadratische fout", + "Info": "De gemiddelde kwadratenfout is het gemiddelde van de kwadraten van de fouten.", + "Short": "Gemiddelde sq. fout", + "Title": "Aanvullende informatie over gemiddelde kwadratische fout" }, "Precision": { - "Name": "Precision score", - "Info": "The precision is the ratio of true positives over all predicted positives.", - "Short": "Precision", - "Title": "Additional information on precision" + "Name": "Precisiescore", + "Info": "De precisie is de verhouding van terecht-positieven ten opzichte van alle voorspelde positieven.", + "Short": "Precisie", + "Title": "Aanvullende informatie over precisie" }, "Recall": { - "Name": "Recall score", - "Info": "The recall is the ratio of true positives over all actual positives.", - "Short": "Recall", - "Title": "Additional information on recall" + "Name": "Score intrekken", + "Info": "De intrekking is de verhouding van terecht-positieven ten opzichte van alle werkelijke positieven.", + "Short": "Terughalen", + "Title": "Aanvullende informatie over intrekken" }, "MacroPrecision": { - "Name": "Macro averaged precision score", - "Info": "The macro averaged precision is the ratio of true positives over all predicted positives computed independently per class and averaged.", - "Short": "Macro precision", - "Title": "Additional information on macro averaged precision" + "Name": "Gemiddelde precisiescore voor macro's", + "Info": "De gemiddelde precisie van de macro is de verhouding van terecht-positieven ten opzichte van alle voorspelde positieven die onafhankelijk per klasse en gemiddeld worden berekend.", + "Short": "Macroprecisie", + "Title": "Aanvullende informatie over gemiddelde precisie van macro's" }, "MicroPrecision": { - "Name": "Micro averaged precision score", - "Info": "The micro averaged precision is the ratio of true positives over all predicted positives aggregated for all classes.", - "Short": "Micro precision", - "Title": "Additional information on micro averaged precision" + "Name": "Gemiddelde precisiescore voor micro's", + "Info": "De microgemiddelde precisie is de verhouding van terecht-positieven ten opzichte van alle voorspelde positieven die voor alle klassen zijn geaggregeerd.", + "Short": "Microprecisie", + "Title": "Aanvullende informatie over gemiddelde intrekking van micro's" }, "MacroRecall": { - "Name": "Macro averaged recall score", - "Info": "The macro averaged recall is the ratio of true positives over all actual positives computed independently per class and averaged.", - "Short": "Macro recall", - "Title": "Additional information on macro averaged recall" + "Name": "Gemiddelde score voor intrekken van macro's", + "Info": "Het microgemiddelde van het intrekken is de verhouding van terecht-positieven ten opzichte van alle werkelijke positieven die onafhankelijk per klasse en gemiddeld worden berekend.", + "Short": "Macro-intrekking", + "Title": "Aanvullende informatie over gemiddelde intrekking van macro's" }, "MicroRecall": { - "Name": "Micro averaged recall score", - "Info": "The micro averaged recall is the ratio of true positives over all actual positives aggregated for all classes.", - "Short": "Micro recall", - "Title": "Additional information on micro averaged recall" + "Name": "Gemiddelde score voor intrekken van micro's", + "Info": "Het microgemiddelde van het intrekken is de verhouding van terecht-positieven ten opzichte van alle werkelijke positieven die voor alle klassen zijn geaggregeerd.", + "Short": "Micro-intrekking", + "Title": "Aanvullende informatie over gemiddelde intrekking van micro's" }, "MacroF1Score": { - "Name": "Macro averaged F1 score", - "Info": "The macro averaged F1 score is the harmonic mean of the macro averaged precision and recall metrics.", - "Short": "Macro F1 score", - "Title": "Additional information on macro averaged F1 score" + "Name": "Gemiddelde F1-score voor macro's", + "Info": "De gemiddelde F1-score voor macro's is het harmonische gemiddelde van de gemiddelde precisie en relevante metrische gegevens van de macro.", + "Short": "Macro F1-score", + "Title": "Aanvullende informatie over de gemiddelde F1-score voor macro's" }, "MicroF1Score": { - "Name": "Micro averaged F1 score", - "Info": "The micro averaged F1 score is the harmonic mean of the micro averaged precision and recall metrics.", - "Short": "Micro F1 score", - "Title": "Additional information on micro averaged F1 score" + "Name": "Microgemiddelde F1-score", + "Info": "De microgemiddelde F1-score is het harmonische gemiddelde van de microgemiddelde precisie en relevante metrische gegevens.", + "Short": "Micro F1-score", + "Title": "Aanvullende informatie over een microgemiddelde F1-score" }, "MeanAveragePrecision": { - "Name": "Mean average precision score", - "Info": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall.", - "Short": "Mean avg precision", - "Title": "Additional information on mean average precision score" + "Name": "Mean-gemiddelde precisiescore", + "Info": "Gemiddelde precisie voor objectdetectiemodellen is het gemiddelde van AP (gemiddelde precisie) in alle klassen. Hiermee wordt de robuustheid van uw objectdetectiemodel geëvalueerd en omsluit de afweging tussen precisie en intrekken.", + "Short": "Mean-gemiddelde precisie", + "Title": "Aanvullende informatie over gemiddelde precisiescore" }, "AveragePrecision": { - "Name": "Average precision score", - "Info": "Average precision for object detection models is calculated for a selected class.", - "Short": "Avg precision", - "Title": "Additional information on average precision score" + "Name": "Gemiddelde precisiescore", + "Info": "Gemiddelde precisie voor objectdetectiemodellen wordt berekend voor een geselecteerde klasse.", + "Short": "Gemiddelde precisie", + "Title": "Aanvullende informatie over de gemiddelde precisiescore" }, "AverageRecall": { - "Name": "Average recall score", - "Info": "Average recall for object detection models is calculated for a selected class.", - "Short": "Avg recall", - "Title": "Additional information on average recall score" + "Name": "Gemiddelde score voor relevante overeenkomsten", + "Info": "Gemiddelde precisie voor objectdetectiemodellen wordt berekend voor een geselecteerde klasse.", + "Short": "Gemiddelde relevante overeenkomsten", + "Title": "Aanvullende informatie over de gemiddelde score voor relevante overeenkomsten" }, "metricName": "Naam van de meetwaarde", "metricValue": "Metrische waarde" }, "MetricSelector": { "selectorLabel": "Metrische waarde selecteren", - "feature1SelectorLabel": "Rows: Feature 1", - "feature2SelectorLabel": "Columns: Feature 2" + "feature1SelectorLabel": "Rijen: functie 1", + "feature2SelectorLabel": "Kolommen: functie 2" }, "Navigation": { "cohortSaved": "Nieuw cohort is opgeslagen. Zie de cohortlijst onder Cohortinstellingen.", @@ -433,9 +433,9 @@ "defaultLabelCopy": "Alle gegevens kopiëren" }, "TreeView": { - "ariaLabel": "Interactive chart", - "disabledArialLabel": "Disabled interactive chart", - "treeMapInfoTitle": "Additional information on tree map", + "ariaLabel": "Interactieve grafiek", + "disabledArialLabel": "Uitgeschakelde interactieve grafiek", + "treeMapInfoTitle": "Aanvullende informatie over structuurkaart", "treeDescription": "De structuurvisualisatie maakt gebruik van de wederzijdse informatie tussen elke functie en de fout om foutexemplaren het beste te scheiden van geslaagde exemplaren hiërarchisch in de gegevens. Dit vereenvoudigt het proces van het detecteren en markeren van veelvoorkomende foutpatronen. Als u belangrijke foutpatronen wilt vinden, zoekt u naar knooppunten met een sterkere rode kleur (bijvoorbeeld hoge foutfrequentie) en een hogere opvullijn (d.w.v. hoge foutdekking). Als u de lijst die in de structuur worden gebruikt met functies wilt bewerken, klikt u op Onderdelenlijst. Gebruik de vervolgkeuzelijst Metrische gegevens selecteren voor meer informatie over de prestaties van uw fout- en succesknooppunten. Houd er rekening mee dat deze selectie van metrische gegevens geen invloed heeft op de manier waarop uw foutstructuur wordt gegenereerd.", "treeStaticDescription": "De structuurvisualisatie maakt gebruik van de wederzijdse informatie tussen elke functie en de fout om, hiërarchisch in de gegevens, foutexemplaren het beste te scheiden van geslaagde exemplaren. Dit vereenvoudigt het proces van het detecteren en markeren van veelvoorkomende foutpatronen. Als u belangrijke foutpatronen wilt vinden, zoekt u naar knooppunten met een sterkere rode kleur (bijvoorbeeld hoge foutfrequentie) en een hogere opvullijn (d.w.v. hoge foutdekking). Als u de lijst die in de structuur worden gebruikt met functies wilt bewerken, klikt u op Functielijst. Gebruik de vervolgkeuzelijst Metrische gegevens selecteren voor meer informatie over de prestaties van uw fout- en succesknooppunten. Houd er rekening mee dat deze selectie van metrische gegevens geen invloed heeft op de manier waarop uw foutstructuur wordt gegenereerd.", "disabledWarning": "De fout-treemap is uitgeschakeld, tenzij het globale cohort wordt omgeschakeld naar 'Alle gegevens' omdat de treemap wordt gegenereerd voor de volledige gegevensset. Schakel terug naar de volledige gegevensset om de fout-treemap te bekijken." @@ -770,7 +770,7 @@ "countHelperText": "Een histogram van het aantal punten", "ditherLabel": "Moet op raster worden weergegeven", "groupByCohort": "Groeperen op cohort", - "logarithmicScaling": "Enable logarithmic scaling", + "logarithmicScaling": "Logaritmische schaal inschakelen", "numOfBins": "Aantal bins", "selectClass": "Klasse selecteren", "selectFeature": "Functie selecteren", @@ -823,11 +823,11 @@ "CohortEditor": { "columns": { "index": "Index", - "dataset": "Dataset", - "predictedY": "Predicted Y", - "trueY": "True Y", - "classificationOutcome": "Classification outcome", - "regressionError": "Error" + "dataset": "Gegevensset", + "predictedY": "Voorspelde Y", + "trueY": "Ware Y", + "classificationOutcome": "Classificatieresultaten", + "regressionError": "Fout" }, "TreatAsCategorical": "Beschouwen als categorisch", "addFilter": "Filter toevoegen", @@ -852,8 +852,8 @@ "save": "Opslaan", "saveAndSwitch": "Opslaan en schakelen", "selectFilter": "Filter selecteren", - "noFiltersApplied": "No filters applied", - "filterAdded": "Filter added" + "noFiltersApplied": "Geen filters toegepast", + "filterAdded": "Filter toegevoegd" }, "Columns": { "classificationOutcome": "Classificatieresultaten", @@ -863,8 +863,8 @@ "falsePositive": "Fout-positief", "none": "Aantal", "predictedProbabilities": "Voorspellingskansen", - "predictedLabels": "Predicted labels", - "trueLabels": "True labels", + "predictedLabels": "Voorspelde labels", + "trueLabels": "Waar-labels", "regressionError": "Regressiefout", "trueNegative": "Terecht-negatief", "truePositive": "Terecht-positief", @@ -885,7 +885,7 @@ "aggregatePlots": "Plots samenvoegen", "chartType": "Grafiektype", "colorValue": "Kleurwaarde", - "infoTitle": "Additional information on data analysis chart view", + "infoTitle": "Aanvullende informatie over de grafiekweergave voor gegevensanalyse", "helperText": "Maak gegevensset-cohorten om gegevenssetstatistieken te analyseren, samen met filters zoals voorspeld resultaat, gegevenssetfuncties en foutgroepen. Meer informatie over de over/onderpresentatie in uw gegevensset.", "individualDatapoints": "Afzonderlijke gegevenspunten", "missingParameters": "Op dit tabblad moet een evaluatiegegevensset worden opgegeven.", @@ -906,6 +906,8 @@ "index": "Index", "output": "Uitvoer", "predictedY": "Voorspelde Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Waarschijnlijkheid: {0}", "trueY": "Terecht-Y", "xValue": "X-waarde:", @@ -974,10 +976,10 @@ "dependencePlotHelperText": "Deze afhankelijkheidsgrafiek toont de relatie van de waarden van een functie met de bijbehorende waarden voor functie-urgentie.", "dependencePlotTitle": "Plots met afhankelijkheden", "helperText": "Verken de belangrijkste functies die van invloed zijn op uw algemene modelvoorspellingen (ook wel globale uitleg). Gebruik de schuifregelaar om aflopende functie-urgenties weer te geven. Alle cohortenfunctie-urgenties worden naast elkaar weergegeven en kunnen worden uitgeschakeld door het cohort in de legenda te selecteren. Klik op een van de functies in de grafiek om een dichtheidsdiagram te zien onder de invloed van de waarden van de geselecteerde functie op de voorspelling.", - "infoTitle": "Additional information on aggregate feature importance", + "infoTitle": "Aanvullende informatie over de geaggregeerde functie-urgentie", "legendHelpText": "Schakel cohorten in en uit in de plot door op de legenda-items te klikken.", "missingParameters": "Op dit tabblad moet de parameter voor de lokale functie-urgentie worden opgegeven.", - "sortByCohort": "Sort by cohort", + "sortByCohort": "Sorteren op cohort", "sortBy": "Sorteren op gegevenspunt", "topAtoB": "Belangrijkste {0} functies op basis van hun belang", "viewDependencePlotFor": "Plot met afhankelijkheden weergeven voor:", @@ -1020,15 +1022,15 @@ }, "Statistics": { "accuracy": "Nauwkeurigheid: {0}", - "bleuScore": "Bleu score: {0}", - "bertScore": "Bert score: {0}", - "exactMatchRatio": "Exact match ratio: {0}", - "rougeScore": "Rouge Score: {0}", + "bleuScore": "Bleu-score: {0}", + "bertScore": "Bert-score: {0}", + "exactMatchRatio": "Exacte overeenkomstverhouding: {0}", + "rougeScore": "Rouge-score: {0}", "fnr": "Percentage fout-negatief: {0}", "fpr": "Percentage fout-positief: {0}", - "hammingScore": "Hamming score: {0}", + "hammingScore": "Hamming-score: {0}", "meanPrediction": "Gemiddelde voorspelling {0}", - "meteorScore": "Meteor Score: {0}", + "meteorScore": "Meteor-score: {0}", "mse": "Gemiddelde kwadratische fout: {0}", "precision": "Precisie: {0}", "rSquared": "R²: {0}", @@ -1036,10 +1038,10 @@ "selectionRate": "Selectiefrequentie: {0}", "mae": "Gemiddelde absolute fout: {0}", "f1Score": "F1-score: {0}", - "samples": "Sample size: {0}", - "meanAveragePrecision": "Mean average precision: {0}", - "averagePrecision": "Average precision: {0}", - "averageRecall": "Average recall: {0}" + "samples": "Voorbeeldgrootte: {0}", + "meanAveragePrecision": "Gemiddelde precisie: {0}", + "averagePrecision": "Gemiddelde precisie: {0}", + "averageRecall": "Gemiddelde intrekking: {0}" }, "ValidationErrors": { "addFilters": "Filters toevoegen", @@ -1147,30 +1149,30 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Dashboard interpreteerbaarheid", - "importantWords": "Show most important words", + "importantWords": "Belangrijkste woorden weergeven", "topFeatureList": "Analyse van lijst met belangrijkste functies", "allButton": "ALLE FUNCTIES", "negButton": "NEGATIEVE FUNCTIES", "posButton": "POSITIEVE FUNCTIES", - "legendText": "Positive scalar feature importances represent the extent that the words were important towards the classification of your selected label, and negative scalar feature importances represent words that encouraged your model away from your selected label.", - "legendTextForQA": "The left text box and the bar chart display the predictions of the model. The right text box shows the feature importance associated with a selected token. Positive feature importances represent the extent that the words were important towards marking the selected token as the starting/ending position of the answer.", + "legendText": "Een positief scalaire functie-urgenties geeft aan in hoeverre de woorden belangrijk waren voor de classificatie van uw geselecteerde label. Een negatief scalaire functie-urgenties geeft aan welke woorden uw model van uw geselecteerde label wegleiden.", + "legendTextForQA": "Het linkertekstvak en het staafdiagram geven de voorspellingen van het model weer. Het rechtertekstvak geeft de functie-urgentie weer die is gekoppeld aan een geselecteerd token. Positieve functie-urgenties geven aan in welke mate de woorden belangrijk waren voor het markeren van het geselecteerde token als de begin-/eindpositie van het antwoord.", "label": "Label", "colon": ": ", - "startingPosition": "STARTING POSITION", - "endingPosition": "ENDING POSITION", - "predictedAnswer": "Predicted answer: ", - "trueAnswer": "True answer: ", - "inputs": "Inputs", - "outputs": "Outputs", - "sliderAriaLabel": "Slider for most important words" + "startingPosition": "BEGINPOSITIE", + "endingPosition": "EINDPOSITIE", + "predictedAnswer": "Voorspeld antwoord: ", + "trueAnswer": "Waar antwoord: ", + "inputs": "Invoer", + "outputs": "Uitvoer", + "sliderAriaLabel": "Schuifregelaar voor de belangrijkste woorden" }, "Legend": { "featureLegend": "LEGENDA VAN TEKSTFUNCTIE", "posFeatureImportance": "POSITIEVE FUNCTIE-URGENTIE", "negFeatureImportance": "NEGATIEVE FUNCTIE-URGENTIE", - "cls": "CLS: start of the sentence", - "sep": "SEP: end of the sentence", - "selectedWord": "Selected word: " + "cls": "CLS: begin van de zin", + "sep": "SEP: einde van de zin", + "selectedWord": "Geselecteerd woord: " }, "BarChart": { "featureImportance": "FUNCTIE-URGENTIE" @@ -1178,59 +1180,59 @@ }, "InterpretVision": { "Cohort": { - "close": "Close", - "errorCohortName": "Please choose a unique cohort name.", - "errorNumSelected": "Please select at least one (1) item.", - "itemsSelectedSingular": "item selected", - "itemsSelectedPlural": "items selected", - "save": "Save cohort", - "saveAndClose": "Save and close", - "saveAndSwitch": "Save and switch", - "textField": "New cohort name", - "title": "Save new cohort" + "close": "Sluiten", + "errorCohortName": "Kies een unieke cohortnaam.", + "errorNumSelected": "Selecteer ten minste één (1) item.", + "itemsSelectedSingular": "item geselecteerd", + "itemsSelectedPlural": "items geselecteerd", + "save": "Cohort opslaan", + "saveAndClose": "Opslaan en sluiten", + "saveAndSwitch": "Opslaan en schakelen", + "textField": "Nieuwe cohortnaam", + "title": "Nieuw cohort opslaan" }, "Dashboard": { "allData": "Alle gegevens", - "columnOne": "Image", + "columnOne": "Afbeelding", "columnTwo": "Index", "columnThree": "Waar Y", "columnFour": "Voorspelde Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Juist", + "columnFourOD": "Onjuist", "columnFive": "Andere metagegevens", - "chooseObject": "Choose a detected object", - "examples": "examples", + "chooseObject": "Een gedetecteerd object kiezen", + "examples": "voorbeelden", "filter": "Filteren", - "indexLabel": "Image ", - "labelTypeDropdown": "Select label type", - "labelVisibilityDropdown": "Select labels to display", - "legendFailure": "failure", - "legendSuccess": "success", - "loading": "Computing explanation for index", + "indexLabel": "Afbeelding ", + "labelTypeDropdown": "Labeltype selecteren", + "labelVisibilityDropdown": "Labels selecteren om weer te geven", + "legendFailure": "mislukte poging", + "legendSuccess": "geslaagd", + "loading": "Uitleg voor index berekenen", "multiselect": "Multiselect", - "notdefined": "object scenario not defined", - "objectSelect": "Object Selection", + "notdefined": "objectscenario niet gedefinieerd", + "objectSelect": "Objectselectie", "pageSize": "Paginaformaat: ", - "panelTitle": "Selected instance", - "panelExplanation": "Explanation", - "panelInformation": "Information", - "predictedLabel": "Predicted label", - "predictedY": "Predicted: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", + "panelTitle": "Geselecteerd exemplaar", + "panelExplanation": "Uitleg", + "panelInformation": "Informatie", + "predictedLabel": "Voorspeld label", + "predictedY": "Voorspeld: ", + "correctDetections": "Juiste detecties: ", + "incorrectDetections": "Onjuiste detecties: ", "prefix": "Object: ", - "rows": "Rows: ", + "rows": "Rijen: ", "search": "Zoeken", - "selectAll": "Select all", + "selectAll": "Alles selecteren", "settings": "Instellingen", - "showAll": "Show all", + "showAll": "Alles weergeven", "tabOptionFirst": "Afbeeldingsverkenner-weergave", "tabOptionSecond": "Tabelweergave", - "tabOptionThird": "Class view", + "tabOptionThird": "Klasseweergave", "thumbnailSize": "Miniatuurgrootte", "titleBarError": "Foutexemplaren", "titleBarSuccess": "Geslaagde exemplaren", - "trueY": "Ground truth: " + "trueY": "Basiswaarheid: " } }, "ModelAssessment": { @@ -1239,15 +1241,15 @@ "CalloutContent": "Door bepaalde onderdelen toe te voegen (foutstructuurweergave, heatmap met fouten) kunt u de gegevens uit de globale cohort filteren die u in de onderstaande onderdelen ziet.", "CalloutTitle": "Onderdeel toevoegen", "TabAddedMessage": { - "DataAnalysis": "Data analysis component added", - "FeatureImportances": "Feature importances component added", - "ErrorAnalysis": "Error analysis component added", - "Fairness": "Fairness component added", - "ModelOverview": "Model overview component added", - "CausalAnalysis": "Causal analysis component added", - "Counterfactuals": "Counterfactuals component added", - "Vision": "Vision data explorer component added", - "Forecasting": "Forecasting what-if component added" + "DataAnalysis": "Onderdeel voor gegevensanalyse toegevoegd", + "FeatureImportances": "Onderdeel voor functie-urgenties toegevoegd", + "ErrorAnalysis": "Onderdeel voor foutanalyse toegevoegd", + "Fairness": "Verdelingsonderdeel toegevoegd", + "ModelOverview": "Modeloverzichtsonderdeel toegevoegd", + "CausalAnalysis": "Oorzakelijk analyseonderdeel toegevoegd", + "Counterfactuals": "Onderdeel Counterfactuals toegevoegd", + "Vision": "Onderdeel toegevoegd aan Vision-gegevensverkenner", + "Forecasting": "Wat-als-onderdeel voor prognoses toegevoegd" } }, "CausalAnalysis": { @@ -1275,7 +1277,7 @@ }, "CohortInformation": { "ShiftCohort": "Cohort wisselen", - "SwitchTimeSeries": "Switch time series", + "SwitchTimeSeries": "Tijdreeks wisselen", "NewCohort": "Nieuwe cohort", "DataPoints": "Aantal gegevenspunten", "DefaultCohort": " (standaard)", @@ -1287,7 +1289,7 @@ "CohortSettingsTitle": "Cohortinstellingen" }, "ComponentNames": { - "ChartView": "Chart view", + "ChartView": "Grafiekweergave", "CausalAnalysis": "Oorzakelijke analyse", "Counterfactuals": "Nulscenario's", "DataAnalysis": "Gegevensanalyse", @@ -1296,10 +1298,10 @@ "ErrorAnalysis": "Foutenanalyse", "Fairness": "Eerlijkheid", "FeatureImportances": "Functie-urgenties", - "Forecasting": "Forecasting", + "Forecasting": "Prognose", "ModelOverview": "Modeloverzicht", - "TableView": "Table view", - "VisionTab": "Vision data explorer" + "TableView": "Tabelweergave", + "VisionTab": "Vision-gegevensverkenner" }, "DashboardSettings": { "Content": "In deze lijst wordt de indeling van het dashboard weergegeven. U kunt gegevens filteren met behulp van het foutanalyseonderdeel, dat u in de onderstaande onderdelen kunt bekijken.", @@ -1458,16 +1460,16 @@ "GlobalExplanation": "Functie-urgentie samenvoegen", "IncorrectPredictions": "Onjuiste voorspellingen", "InfoTitle": "Additional information on feature importance values", - "IndividualFeatureTabular": "Select a datapoint by clicking on a datapoint (up to 5 datapoints) in the table to view their local feature importance values (local explanation) and individual conditional expectation (ICE) plots.", + "IndividualFeatureTabular": "Selecteer een gegevenspunt door op een gegevenspunt (maximaal 5 gegevenspunten) in de tabel te klikken om de waarden voor de lokale functie-urgentie (lokale uitleg) en de afzonderlijke ICE-plot (voorwaardelijke verwachting) weer te geven.", "IndividualFeatureText": "Select a datapoint by clicking on a datapoint in the table to view the local feature importance values (local explanation) from SHAP's text explainer.", "LocalExplanation": "Belang van afzonderlijke functies", "SelectionCounter": "{0}/{1} gegevenspunten geselecteerd", "SelectionLimit": "Er kunnen momenteel maximaal 5 gegevenspunten worden geselecteerd.", - "RowCheckboxAriaLabel": "Row checkbox", - "SelectionColumnAriaLabel": "Toggle selection" + "RowCheckboxAriaLabel": "Selectievakje Rij", + "SelectionColumnAriaLabel": "Selectie in-/uitschakelen" }, "IndividualFeatureImportanceView": { - "SmallInstanceSelection": "Instance selection" + "SmallInstanceSelection": "Exemplaar selecteren" }, "MainMenu": { "DashboardSettings": "Dashboardconfiguratie", @@ -1483,44 +1485,44 @@ "ModelOverview": { "metrics": { "accuracy": { - "name": "Accuracy score", + "name": "Nauwkeurigheidsscore", "description": "Het deel van de gegevenspunten dat op de juiste wijze is ingedeeld." }, "exactMatchRatio": { - "name": "Exact match ratio", - "description": "The ratio of instances classified correctly for every label." + "name": "Exacte overeenkomstverhouding", + "description": "De verhouding van exemplaren die correct zijn geclassificeerd voor elk label." }, "meteorScore": { - "name": "Meteor Score", - "description": "METEOR Score is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision in question answering task." + "name": "Meteor-score", + "description": "De METEOR-score wordt berekend op basis van het harmonische gemiddelde van precisie en intrekken, waarbij intrekken meer gewicht heeft dan precisie bij een vraagsbeantwoordingstaak." }, "bleuScore": { - "name": "Bleu Score", - "description": "Bleu Score measures the ratio of words (and/or n-grams) in the machine generated text that appeared in the reference text in question answering task." + "name": "Bleu-score", + "description": "Met de Bleu-score wordt de verhouding gemeten tussen woorden (en/of n-grammen) in de door de computer gegenereerde tekst die voorkomt in de referentietekst in een vraagsbeantwoordingstaak." }, "bertScore": { - "name": "Bert Score", - "description": "BERTScore focuses on computing semantic similarity between tokens of reference and machine generated text in question answering task." + "name": "Bert-score", + "description": "BERTScore richt zich op het berekenen van semantische overeenkomsten tussen referentietokens en door de computer gegenereerde tekst in een vraagsbeantwoordingstaak." }, "rougeScore": { - "name": "Rouge Score", - "description": "Rouge Score measures the ratio of words (and/or n-grams) in the reference text that appeared in the machine generated text in question answering task." + "name": "Rouge-score", + "description": "Met de Rouge-score wordt de verhouding gemeten tussen woorden (en/of n-grammen) in de door de computer gegenereerde tekst in een vraagsbeantwoordingstaak." }, "hammingScore": { - "name": "Hamming score", - "description": "The average ratio of labels classified correctly among those classified as 1 in multilabel task." + "name": "Hamming-score", + "description": "De gemiddelde verhouding van labels die correct zijn geclassificeerd waaronder die zijn geclassificeerd als 1 in een taak met meerdere labels." }, "f1Score": { "name": "F1-score", "description": "De F1-score is het harmonische gemiddelde van precisie en treffactor." }, "f1ScoreMacro": { - "name": "Macro F1 score", - "description": "Macro F1 score is the harmonic mean of precision and recall for each class, with each class weighted equally." + "name": "Macro F1-score", + "description": "Macro F1-score is het harmonische gemiddelde van precisie en intrekken voor elke klasse, waarbij elke klasse gelijk wordt gewogen." }, "f1ScoreMicro": { - "name": "Micro F1 score", - "description": "Micro F1 score is the harmonic mean of precision and recall for each class, with each class weighted according to how many instances it contains." + "name": "Micro F1-score", + "description": "Micro F1-score is het harmonische gemiddelde van precisie en intrekken voor elke klasse, waarbij elke klasse wordt gewogen op basis van het aantal exemplaren dat deze bevat." }, "meanAbsoluteError": { "name": "Gemiddelde absolute fout", @@ -1535,24 +1537,24 @@ "description": "Het deel van de gegevenspunten dat correct is geclassificeerd van alle gegevenspunten die als 1 zijn geclassificeerd." }, "precisionMacro": { - "name": "Macro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted equally." + "name": "Macroprecisiescore", + "description": "Het deel van de gegevenspunten dat correct is geclassificeerd waaronder die als 1 zijn geclassificeerd, voor elke klasse met elke klasse en dat gelijk is gewogen." }, "precisionMicro": { - "name": "Micro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted according to how many instances it contains." + "name": "Microprecisiescore", + "description": "Het deel van de gegevenspunten dat correct is geclassificeerd waaronder die zijn geclassificeerd als 1 voor elke klasse met elke klasse, waarbij elke klasse wordt gewogen op basis van het aantal exemplaren dat het bevat." }, "recall": { "name": "Score intrekken", "description": "Het deel van de gegevenspunten dat correct is geclassificeerd van alle gegevenspunten waarvan het werkelijke label 1 is. Alternatieve benamingen: frequentie terecht-positieven, gevoeligheid." }, "recallMacro": { - "name": "Macro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted equally." + "name": "Score voor intrekken van macro's", + "description": "Het deel van de gegevenspunten dat correct is geclassificeerd waaronder die waarvan het label Waar 1 is voor elke klasse met elke klasse en dat gelijk is gewogen." }, "recallMicro": { - "name": "Micro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted according to how many instances it contains." + "name": "Score voor intrekken van micro's", + "description": "Het deel van de gegevenspunten dat correct is geclassificeerd waaronder die waarvan het label Waar 1 is voor elke klasse met elke klasse, gewogen op basis van het aantal exemplaren dat het bevat." }, "falsePositiveRate": { "name": "Percentage fout-positief", @@ -1571,32 +1573,32 @@ "description": "Het gemiddelde van alle voorspellingen." }, "meanAveragePrecision": { - "name": "Mean Average Precision score", - "description": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall." + "name": "Gemiddelde precisiescore", + "description": "Gemiddelde precisie voor objectdetectiemodellen is het gemiddelde van AP (gemiddelde precisie) in alle klassen. Hiermee wordt de robuustheid van uw objectdetectiemodel geëvalueerd en omsluit de afweging tussen precisie en intrekken." }, "averagePrecision": { - "name": "Average Precision score", - "description": "Average precision for object detection models is calculated for a selected class." + "name": "Gemiddelde precisiescore", + "description": "Gemiddelde precisie voor objectdetectiemodellen wordt berekend voor een geselecteerde klasse." }, "averageRecall": { - "name": "Average Recall score", - "description": "Average recall for object detection models is calculated for a selected class." + "name": "Gemiddelde score voor intrekken", + "description": "Gemiddelde precisie voor objectdetectiemodellen wordt berekend voor een geselecteerde klasse." }, "fairnessMetricDifference": "Verschil", "fairnessMetricRatio": "Verhouding" }, "metricsDropdown": "Metrische waarde(n)", - "metricsTypeDropdown": "Aggregate method", + "metricsTypeDropdown": "Aggregatiemethode", "metricTypes": { "macro": "Macro", "micro": "Micro" }, - "classSelectionDropdown": "Select class(es)", + "classSelectionDropdown": "Klasse(n) selecteren", "iouThresholdDropdown": { - "name": "IoU Threshold", - "description": "Intersection over Union quantifies the degree of overlap between the prediction and ground truth bounding box of a detected object in an image. For example, setting an IoU threshold of 70% means that a prediction with greater than 70% overlap with ground truth is True, thus influencing the definition of prediction correctness and calculation of other performance metrics.", + "name": "IoU-drempelwaarde", + "description": "Doorsnede over Vereniging is de mate van overlap tussen de voorspelde en de basiswaarheidsgrens van een gedetecteerd object in een afbeelding. Als een IoU-drempel bijvoorbeeld wordt ingesteld op 70%, houdt dit in dat een voorspelling met een overlap van meer dan 70% met de basiswaarheid Waar is en dit is van invloed is op de definitie van de juistheid van de voorspelling en de berekening van andere metrische prestatiegegevens.", "iconId": "iouThresholdIconId", - "title": "Learn about the IoU threshold" + "title": "Meer informatie over de IoU-drempel" }, "notAvailable": "N.v.t.", "countColumnHeader": "Steekproefgrootte", @@ -1608,14 +1610,14 @@ "featuresDropdown": "Functie(s)", "metricChartDropdownSelectionHeader": "Metrische gegevens", "probabilityForClassSelectionHeader": "Waarschijnlijkheid voor klasse", - "targetSelectionHeader": "Target", + "targetSelectionHeader": "Doel", "metricSelectionDropdownPlaceholder": "Selecteer metrische gegevens om uw cohorten te vergelijken.", - "classSelectionDropdownPlaceholder": "Select class name for class-based analysis.", + "classSelectionDropdownPlaceholder": "Selecteer de klassenaam voor analyse op basis van klassen.", "featureSelectionDropdownPlaceholder": "Selecteer functies die u wilt gebruiken voor een analyse op basis van functies.", "probabilityDistributionPivotItem": "Kansverdeling", - "regressionDistributionPivotItem": "Target distribution", + "regressionDistributionPivotItem": "Doeldistributie", "metricsVisualizationsPivotItem": "Visualisaties van metrische gegevens", - "confusionMatrixPivotItem": "Confusion matrix", + "confusionMatrixPivotItem": "Verwarringsmatrix", "disaggregatedAnalysisFeatureSelectionPlaceholder": "Selecteer functies om de analyse op basis van functies te genereren.", "tableCountTooltip": "Cohort {0} bevat {1} instanties.", "tableMetricTooltip": "De {0} van het model op cohort {1} is {2}", @@ -1626,36 +1628,36 @@ "metricSelectionButton": "Metrische gegevens kiezen", "cohortSelectionButton": "Cohorten kiezen", "probabilityLabelSelectionButton": "Label kiezen", - "regressionTargetSelectionButton": "Choose target", + "regressionTargetSelectionButton": "Doel kiezen", "selectAllCohortsOption": "Alles selecteren", "other": "Overige", "BoxPlot": { "outlierProbability": "waarschijnlijkheid", "outlierLabel": "Uitbijters", "boxPlotSeriesLabel": "Boxplot", - "lowerWhisker": "Lower whisker", - "upperWhisker": "Upper whisker", - "median": "Median", - "lowerQuartile": "Lower quartile", - "upperQuartile": "Upper quartile" + "lowerWhisker": "Onderste whisker", + "upperWhisker": "Bovenste whisker", + "median": "Mediaan", + "lowerQuartile": "Onderste kwartiel", + "upperQuartile": "Bovenste kwartiel" }, "chartConfigApply": "Toepassen", "chartConfigCancel": "Annuleren", "chartConfigDatasetCohortSelectionPlaceholder": "Cohorten voor gegevensset selecteren", "chartConfigFeatureBasedCohortSelectionPlaceholder": "Cohorten op basis van functies selecteren", "confusionMatrix": { - "confusionMatrixCohortSelectionLabel": "Select dataset cohort", - "confusionMatrixClassSelectionLabel": "Select classes", - "confusionMatrixClassMinSelectionError": "Select at least {0} classes to visualize the confusion matrix.", - "confusionMatrixClassMaxSelectionError": "Select at most {0} classes to visualize the confusion matrix.", - "confusionMatrixClassSelectionDefaultPlaceholder": "Choose classes", - "confusionMatrixHeatmapTooltip": "{0} datapoints should be {1}, predicted to be {2}", - "confusionMatrixYAxisLabel": "True Class", - "confusionMatrixXAxisLabel": "Predicted Class", - "class": "Class" + "confusionMatrixCohortSelectionLabel": "Cohort voor gegevensset selecteren", + "confusionMatrixClassSelectionLabel": "Klassen selecteren", + "confusionMatrixClassMinSelectionError": "Selecteer ten minste {0} klassen om de verwarringsmatrix te visualiseren.", + "confusionMatrixClassMaxSelectionError": "Selecteer ten minste {0} klassen om de verwarringsmatrix te visualiseren.", + "confusionMatrixClassSelectionDefaultPlaceholder": "Klassen kiezen", + "confusionMatrixHeatmapTooltip": "{0} gegevenspunten moeten worden {1}, voorspeld als {2}", + "confusionMatrixYAxisLabel": "Klasse Waar", + "confusionMatrixXAxisLabel": "Voorspelde klasse", + "class": "Klasse" }, "nA": "N.v.t.", - "disaggregatedAnalysisBaseCohortDisclaimer": "The cohorts in the following feature-based analysis are based on the global cohort, {0}.", + "disaggregatedAnalysisBaseCohortDisclaimer": "De cohorten in de volgende analyse op basis van functies zijn gebaseerd op het globale cohort, {0}.", "disaggregatedAnalysisBaseCohortWarning": "In tegenstelling tot het {0} cohort bevat {1} filters. Als gevolg hiervan wordt alleen een subset van de hele gegevensset vastgelegd en kunnen inzichten mogelijk niet generaliseren naar de volledige gegevensset.", "probabilitySplineChartToggleLabel": "Spline-grafiek gebruiken", "countAxisLabel": "Aantal", @@ -1685,76 +1687,76 @@ "flyoutDescription": "U kunt ervoor kiezen om cohorten van gegevenssets of van functies weer te geven. Als functie-cohorten niet beschikbaar zijn, moet u eerst een of meer functies selecteren in de weergave voor functie-cohorten. Vervolgens worden functie-cohorten gegenereerd en kunt u deze hier selecteren." }, "regressionTargetOptions": { - "predictedY": "Predicted Y", - "trueY": "True Y", - "error": "Error" + "predictedY": "Voorspelde Y", + "trueY": "Ware Y", + "error": "Fout" }, "topLevelDescription": "Evalueer de prestaties van uw model door de distributie van uw voorspellingswaarden en de waarden van de metrische gegevens over de prestaties van uw model te verkennen. Gebruik het tabblad Gegevensset-cohorten om uw model te onderzoeken door te kijken naar een relatieve analyse van de prestaties in verschillende vooraf gebouwde of nieuw gemaakte gegevensset-cohorten. Gebruik de Functie-cohorten om uw model te onderzoeken door te kijken naar een relatieve analyse van de prestaties van gevoelige/niet-gevoelige Functie-subcohorten. (bijvoorbeeld prestaties voor verschillende geslachten, inkomstenniveaus).", - "infoTitle": "Additional information on model overview", + "infoTitle": "Aanvullende informatie over modeloverzicht", "visualDisplayToggleLabel": "Heatmap weergeven", "featureBasedViewDescription": "Selecteer maximaal twee functies om de uitsplitsing van de modelprestaties te zien over op functies gebaseerde cohorten (als één functie is geselecteerd) of intersectionele cohorten (als er twee functies zijn geselecteerd)." }, "TableViewTab": { - "Heading": "View the dataset in a table format for all features and rows." + "Heading": "De gegevensset weergeven in een tabelindeling voor alle functies en rijen." } }, "Forecasting": { - "target": "Target", - "whatIfForecastingHeader": "What-if analysis", - "forecastHeader": "Forecast analysis", - "whatIfForecastingDescription": "What-if allows you to perturb features for your entire time series and observe how the model's forecast changes.", - "whatIfForecastingChooseTimeSeries": "To start, choose a time series from the options below.", - "forecastDescription": "Forecast analysis compares your model's forecast to the actual values of your time series. To enable what-if analysis, provide a dataset with features.", - "timeSeries": "Time series", - "selectTimeSeries": "Select a time series.", - "singleTimeSeries": "The dataset contains only a single time series '{0}' which has been selected by default.", - "trueY": "True Y", - "baselinePrediction": "Baseline prediction", - "forecastComparisonHeader": "Compare What-if Forecasts", - "forecastComparisonChartTitle": "Forecasts", - "forecastComparisonChartTimeAxisLabel": "Time", + "target": "Doel", + "whatIfForecastingHeader": "Wat-als-analyse", + "forecastHeader": "Prognoseanalyse", + "whatIfForecastingDescription": "Met Wat-als kunt u functies voor uw hele tijdreeks verstoren en zien hoe de prognose van het model verandert.", + "whatIfForecastingChooseTimeSeries": "Kies een tijdreeks uit de onderstaande opties om te beginnen.", + "forecastDescription": "De prognoseanalyse vergelijkt de prognose van uw model met de werkelijke waarden van uw tijdreeks. Geef een gegevensset met functies op om wat-als-analyse in te schakelen.", + "timeSeries": "Tijdreeks", + "selectTimeSeries": "Een tijdreeks selecteren.", + "singleTimeSeries": "De gegevensset bevat slechts één tijdreeks {0} die standaard is geselecteerd.", + "trueY": "Ware Y", + "baselinePrediction": "Basislijnvoorspelling", + "forecastComparisonHeader": "Wat-als-prognoses vergelijken", + "forecastComparisonChartTitle": "Prognoses", + "forecastComparisonChartTimeAxisLabel": "Tijd", "Transformations": { - "multiply": "multiply", - "divide": "divide", - "add": "add", - "subtract": "subtract", - "change": "change to" + "multiply": "vermenigvuldigen", + "divide": "delen", + "add": "optellen", + "subtract": "aftrekken", + "change": "wijzigen in" }, "TransformationCreation": { - "title": "Create what-if scenario", - "nameLabel": "What-if scenario name", - "featureInstructions": "Choose a feature to perturb.", - "operationInstructions": "Choose an operation to apply to the feature.", - "operationDropdownHeader": "Operation", - "featureDropdownHeader": "Feature", - "valueSpinButtonHeader": "Value", - "scenarioNamingInstructionsPlaceholder": "Enter a unique name", - "scenarioNamingInstructions": "Enter a name for your what-if scenario.", - "scenarioNamingCollisionMessage": "This name exists already. Please enter a unique name.", - "scenarioNamingLengthMessage": "The name must be between 1 and 50 characters. The actual length is {0}.", - "scenarioNamingInvalidCharactersMessage": "The name can only contain alphanumeric characters, whitespaces, dashes, or underscores, and needs to start with an alphanumeric character.", - "valueErrorMessage": "For operation {0} please select a value other than {1}.", - "invalidCombinationErrorMessage": "This is identical to an existing what-if scenario. Please change the feature, operation, or value.", - "addTransformationButton": "Add Transformation", - "divisionAndMultiplicationBy": "by" + "title": "Wat-als-scenario maken", + "nameLabel": "Wat-als-scenarionaam", + "featureInstructions": "Kies een functie die u wilt verstoren.", + "operationInstructions": "Kies een bewerking om toe te passen op de functie.", + "operationDropdownHeader": "Bewerking", + "featureDropdownHeader": "Functie", + "valueSpinButtonHeader": "Waarde", + "scenarioNamingInstructionsPlaceholder": "Voer een unieke naam in", + "scenarioNamingInstructions": "Voer een naam in voor uw wat-als-scenario.", + "scenarioNamingCollisionMessage": "Deze naam bestaat al. Voer een unieke naam in.", + "scenarioNamingLengthMessage": "De naam moet tussen 1 en 50 tekens lang zijn. De huidige lengte is {0}.", + "scenarioNamingInvalidCharactersMessage": "De naam mag alleen alfanumerieke tekens, spaties, streepjes of onderstrepingstekens bevatten en moet beginnen met een alfanumeriek teken.", + "valueErrorMessage": "Selecteer voor de bewerking {0} selecteert u een andere waarde dan {1}.", + "invalidCombinationErrorMessage": "Dit is identiek aan een bestaand wat-als-scenario. Wijzig de functie, bewerking of waarde.", + "addTransformationButton": "Transformatie toevoegen", + "divisionAndMultiplicationBy": "per" }, "TransformationTable": { - "nameColumnHeader": "Name", - "methodColumnHeader": "Method", - "divisionAndMultiplicationBy": "by ", - "header": "What-if Forecasts ({0})" + "nameColumnHeader": "Naam", + "methodColumnHeader": "Methode", + "divisionAndMultiplicationBy": "per ", + "header": "Wat als-prognoses ({0})" }, "TimeSeries": { - "apply": "Apply", - "cancel": "Cancel", - "cohortList": "Time series list", - "selectCohort": "Select a time series", - "shiftCohort": "Switch time series", - "shiftCohortDescription": "Select a time series from the time series list. Apply the time series to the dashboard." + "apply": "Toepassen", + "cancel": "Annuleren", + "cohortList": "Lijst met tijdreeksen", + "selectCohort": "Een tijdreeks selecteren", + "shiftCohort": "Tijdreeks wisselen", + "shiftCohortDescription": "Selecteer een tijdreeks in de lijst met tijdreeksen. Pas de tijdreeks toe op het dashboard." }, "TimeSeriesSettings": { - "CohortSettingsDescription": "Time series are pre-defined based on time series identifying columns.", - "CohortSettingsTitle": "Time series settings" + "CohortSettingsDescription": "Tijdreeksen zijn vooraf gedefinieerd op basis van tijdreeksen die kolommen identificeren.", + "CohortSettingsTitle": "Tijdreeksinstellingen" } } } \ No newline at end of file diff --git a/libs/localization/src/lib/en.pl.json b/libs/localization/src/lib/en.pl.json index 07d79f8b12..773c475efd 100644 --- a/libs/localization/src/lib/en.pl.json +++ b/libs/localization/src/lib/en.pl.json @@ -3,26 +3,26 @@ "close": "Zamknij", "tooltipButton": "Przycisk etykietki narzędzia", "identityFeature": "Funkcja tożsamości", - "infoTitle": "Additional information", - "spinButton": "Spin", - "editButton": "Edit", - "decreaseValue": "Decrease value", - "increaseValue": "Increase value", - "decreaseValueByOne": "Decrease value by 1", - "increaseValueByOne": "Increase value by 1", - "loading": "Loading..." + "infoTitle": "Dodatkowe informacje", + "spinButton": "Pokrętło", + "editButton": "Edytuj", + "decreaseValue": "Zmniejsz wartość", + "increaseValue": "Zwiększ wartość", + "decreaseValueByOne": "Zmniejsz wartość o 1", + "increaseValueByOne": "Zwiększ wartość o 1", + "loading": "Trwa ładowanie..." }, "ChartContextMenu": { - "hideData": "Hide data table", - "viewData": "View data table", - "viewInFullScreen": "View in full screen", - "printChart": "Print chart", - "downloadCSV": "Download CSV", - "downloadPNG": "Download PNG image", - "downloadJPEG": "Download JPEG image", - "downloadPDF": "Download PDF document", - "downloadSVG": "Download SVG vector image", - "downloadXLS": "Download XLS" + "hideData": "Ukryj tabelę danych", + "viewData": "Wyświetl tabelę danych", + "viewInFullScreen": "Wyświetl na pełnym ekranie", + "printChart": "Drukuj wykres", + "downloadCSV": "Pobierz plik CSV", + "downloadPNG": "Pobierz obraz PNG", + "downloadJPEG": "Pobierz obraz JPEG", + "downloadPDF": "Pobierz dokument PDF", + "downloadSVG": "Pobierz obraz wektora SVG", + "downloadXLS": "Pobierz plik XLS" }, "CausalAnalysis": { "AggregateView": { @@ -39,7 +39,7 @@ "description": "Analiza przyczyn odpowiada na pytania „co-jeśli” na temat tego, jak zmieniłyby się rzeczywiste wyniki w przypadku dokonania wyboru innych zasad, np. innej strategii cenowej w przypadku danego produktu lub alternatywnego sposobu działania w przypadku pacjenta. W przeciwieństwie do przewidywań modelu, które identyfikują ważne wzorce korelacji, narzędzia te pomagają zidentyfikować najważniejsze funkcje przyczynowe, które bezpośrednio wpływają na wynik będący przedmiotem zainteresowania. Modele te identyfikują skutek przyczynowy jednej funkcji (zazwyczaj określanej mianem „działanie”), jednocześnie utrzymując inne funkcje wprowadzające w błąd na stałym poziomie. Aby uzyskać najlepsze wyniki, należy zapewnić, że pełny zestaw danych zawiera wszystkie dostępne funkcje, które mogą korelować z wynikiem jako czynnikiem wprowadzającym w błąd.", "directAggregate": "Bezpośredni zagregowany efekt przyczyn każdego leczenia z 95% przedziałem ufności", "here": "tutaj", - "infoTitle": "Additional information on aggregated causal effects", + "infoTitle": "Dodatkowe informacje na temat zagregowanych efektów przyczynowych", "lasso": "Element Lasso (lub regresja logistyczna, jeśli wartość y jest binarna) był dopasowany do przewidywania y z X[-i], a Lasso (lub regresja logistyczna, jeśli X[i] jest kategoryczne) jest dopasowane do przewidywania X[i] z Χ[-i]. Skutek przyczynowy można wyświetlić jako średnią korelację pozostałości/niespodziewanych wahań dwóch zadań przewidywania. Dowiedz się więcej o podwójnym uczeniu maszynowym", "unconfounding": "Czy są funkcje wprowadzające w błąd?" }, @@ -51,7 +51,7 @@ "description": "Poszczególne skutki przyczynowe mogą informować o spersonalizowanych interwencjach, takich jak promocja ukierunkowana na docelową grupę klientów lub zindywidualizowany plan działania. W jaki sposób osoba z określonym zestawem funkcji będzie reagować na zmianę funkcji przyczynowej lub sposobu działania? Przyczynowe narzędzie warunkowe oblicza marginalne zmiany w rzeczywistych wartościach w przypadku konkretnej osoby, jeśli zmieni się poziom działania. Ta analiza umożliwia zrozumienie, w jaki sposób wyniki rzeczywiste zmieniłyby się w przypadku dokonania wyboru innych zasad, np. innej strategii cenowej w przypadku danego produktu lub alternatywnego sposobu działania w przypadku pacjenta. Określ sposób działania, będący przedmiotem zainteresowania, i obserwuj, jak rzeczywisty wynik mógłby się zmienić.", "directIndividual": "Bezpośredni indywidualny efekt przyczyn każdego leczenia z 95% przedziałem ufności", "index": "Indeks punktów danych", - "infoTitle": "Additional information on individual causal what-if", + "infoTitle": "Dodatkowe informacje o poszczególnych przyczynowych warunkach analizy warunkowej", "missingParameters": "Ta karta wymaga dostarczenia zestawu danych oceny.", "newOutcome": "Nowy wynik", "selectTreatment": "Wybierz rodzaj leczenia", @@ -85,7 +85,7 @@ "averageGainBinary": "Średnie zyski ustawienia działania {0} na jego wartość punktu odniesienia {1}.", "averageGainContinuous": "Średnie zyski z alternatywnych zasad w porównaniu z brakiem leczenia „{0}”.", "header": "Te narzędzia ułatwiają opracowywanie zasad dla przyszłych interwencji. Możesz określić, które części Twojej próbki najbardziej są podatne na zmiany funkcji przyczynowych lub działań, a także utworzyć reguły w celu określenia, które przyszłe zestawy danych powinny być docelowe na potrzeby konkretnych interwencji.", - "infoTitle": "Additional information on treatment policy", + "infoTitle": "Dodatkowe informacje na temat zasad leczenia", "nSample": "n = {0}", "noData": "Brak danych" } @@ -116,8 +116,8 @@ "cancel": "Anuluj", "title": "Przełącz kohortę", "subText": "Wybierz kohortę z listy kohort. Zastosuj kohortę do pulpitu nawigacyjnego.", - "selectCohort": "Select a cohort", - "cohortList": "Cohort list" + "selectCohort": "Wybieranie kohortę", + "cohortList": "Lista kohort" }, "PreBuiltCohort": { "featureNameNotFound": "Nie znaleziono nazwy funkcji w zestawie danych", @@ -148,13 +148,13 @@ "predictedClass": "Przewidywana klasa", "predictedValue": "Wartość przewidywana" }, - "Size": "Size", - "loading": "Loading...", + "Size": "Rozmiar", + "loading": "Trwa ładowanie...", "counterfactualEx": "Przykład hipotezy przeciwnej {0}", "counterfactualName": "Nazwa dla analizy co-jeżeli hipotez przeciwnych", "createWhatIfCounterfactual": "Utwórz hipotezę przeciwną analizy co-jeżeli", "createCounterfactual": "Hipoteza przeciwna", - "revertToBubbleChart": "View bubble chart", + "revertToBubbleChart": "Wyświetl wykres bąbelkowy", "createOwn": "Utwórz własne hipotezy przeciwne:", "currentClass": "Bieżąca klasa", "currentRange": "Bieżący zakres", @@ -167,9 +167,9 @@ "listDescription": "Na tej liście pokazano, które punkty danych w bieżącej próbce mają największą odpowiedź przyczynową na wybrane działanie na podstawie wszystkich funkcji uwzględnionych w szacowanym modelu przyczynowym. Lewe pięć kolumn informuje, czy dany sposób działania jest zalecany dla obserwacji, bieżącego działania, oszacowanego skutku działania (skutku zastosowania działania z punktu odniesienia braku działania w przypadku działań binarnych lub zwiększenia/zmniejszenia funkcji działania o 10% wielkości typowego działania w próbce: [dynamiczne: informuje o zmianie liczbowej w użytym przez nas działaniu]) oraz dolne i górne Interwały pewności (CI) dla tego skutku. Pozostałe kolumny przedstawiają bieżący stan działania i inne funkcje każdej obserwacji.", "localImportanceDescription": "Najważniejsze funkcje w wierszu {0}, aby osiągnąć wymagane przewidywanie modelu. Na podstawie analizy warunkowej w przypadku przewidywania: {1}", "localImportanceSelectData": "Wybierz punkt danych do wyświetlenia lokalnego wykresu ważności", - "largeLocalImportanceSelectData": "Select a bubble, followed by a data point to view local importance chart", - "localImportanceFetchError": "There was an error while fetching the local importance data. Error details: {0} Please check the data used.", - "BubbleChartFetchError": "There was an error while fetching the data. Error details: {0} Please check the data used.", + "largeLocalImportanceSelectData": "Wybierz bąbelek, a następnie punkt danych, aby wyświetlić lokalny wykres ważności", + "localImportanceFetchError": "Wystąpił błąd podczas pobierania danych ważności lokalnej. Szczegóły błędu: {0}. Sprawdź używane dane.", + "BubbleChartFetchError": "Wystąpił błąd podczas pobierania danych. Szczegóły błędu: {0}. Sprawdź używane dane.", "noData": "Brak danych", "noFeatures": "Brak dostępnych funkcji", "panelDescription": "Przeglądaj hipotezy przeciwne i twórz własne. Przeszukuj funkcje, aby zobaczyć sugerowane wartości z różnych zestawów przykładów hipotez przeciwnych. Ustaw sugerowane wartości hipotezy przeciwnej, klikając pole tekstowe „Ustaw wartość” pod każdą nazwą hipotezy przeciwnej. Nazwij Twoją hipotezę przeciwną i zapisz ją.", @@ -223,13 +223,13 @@ "subText": "Dowiedz się więcej o wybranej kohorcie. Edytuj nazwę kohorty. Usuń tę kohortę." }, "FeatureList": { - "featureList": "Feature List", + "featureList": "Lista funkcji", "apply": "Zastosuj", "features": "Cechy", "importances": "Ważności", "treeMapDescription": "Aby ponownie przeszkolić mapę drzewa, wybierz i zapisz poniższe cechy. Ważności cech zostały obliczone na podstawie wspólnych informacji z błędem na etykiecie prawdziwości. Używaj ich jako wskazówki dotyczącej szkolenia mapy drzewa.", "staticTreeMapDescription": "Wyświetl funkcje, które zostały użyte do wytrenowania mapy drzewa. Istotności funkcji zostały obliczone przy użyciu wspólnych informacji z błędem w etykietach true.", - "searchResultMessage": "Results displayed out of {resultLength} for {searchValue}" + "searchResultMessage": "Wyniki wyświetlone z {resultLength} dla {searchValue}" }, "TreeViewParameters": { "maximumDepth": "Maksymalna głębokość", @@ -295,7 +295,7 @@ "disabledWarning": "Mapa cieplna błędów jest wyłączona, chyba że dla kohorty globalnej przełączono się w tryb reprezentowania wartości „Wszystkie dane”, ponieważ mapa cieplna jest generowana dla pełnego zestawu danych. Przełącz się z powrotem w tryb pełnego zestawu danych, aby wyświetlić mapę cieplną błędów." }, "MatrixSummary": { - "heatMapInfoTitle": "Additional information on heat map", + "heatMapInfoTitle": "Dodatkowe informacje na temat mapy cieplnej", "heatMapDescription": "Dzięki mapie cieplnej możesz skoncentrować uwagę na określonych filtrach funkcji międzysekcyjnej i obliczyć wskaźniki rozdzielonych błędów. Zacznij od dwóch funkcji zestawów danych w celu porównania.", "heatMapStaticDescription": "Dzięki mapie cieplnej możesz skoncentrować uwagę na określonych filtrach funkcji międzysekcyjnych i obliczyć wskaźniki rozdzielonych błędów. Przed wyświetleniem pulpitu nawigacyjnego należy wybrać maksymalnie dwie funkcje, aby utworzyć mapę cieplną za pośrednictwem zestawu SDK." }, @@ -311,108 +311,108 @@ }, "Metrics": { "AccuracyScore": { - "Name": "Accuracy score", - "Info": "The accuracy score represents the ratio of correct to total instances in the data.", - "Short": "Accuracy", - "Title": "Additional information on accuracy score" + "Name": "Ocena dokładności", + "Info": "Ocena dokładności reprezentuje stosunek poprawnych do łącznej liczby wystąpień w danych.", + "Short": "Dokładność", + "Title": "Dodatkowe informacje o ocenie dokładności" }, "ErrorRate": { - "Name": "Error rate", - "Info": "The error rate represents the percentage of instances in the node for which the system has failed.", - "Short": "Error rate", - "Title": "Additional information on error rate" + "Name": "Częstotliwość błędów", + "Info": "Częstotliwość błędów oznacza procent wystąpień w węźle, w przypadku których w systemie wystąpił błąd.", + "Short": "Częstotliwość błędów", + "Title": "Dodatkowe informacje na temat częstotliwości błędów" }, "F1Score": { - "Name": "F1 score", - "Info": "The F1 score is the harmonic mean of the precision and recall metrics.", - "Short": "F1 score", - "Title": "Additional information on F1 score" + "Name": "Miara F1", + "Info": "Miara F1 to średnia harmoniczna precyzji i kompletności metryki.", + "Short": "Miara F1", + "Title": "Dodatkowe informacje na temat miary F1" }, "MeanAbsoluteError": { - "Name": "Mean absolute error", - "Info": "The mean absolute error is the average of the sum of the errors.", - "Short": "Mean abs. error", - "Title": "Additional information on mean absolute error" + "Name": "Średni błąd bezwzględny", + "Info": "Średni błąd bezwzględny jest średnią sumy błędów.", + "Short": "Średni błąd bezwzględny", + "Title": "Dodatkowe informacje o średnim błędzie bezwzględnym" }, "MeanSquaredError": { - "Name": "Mean squared error", - "Info": "The mean squared error is the average of the squares of the errors.", - "Short": "Mean sq. error", - "Title": "Additional information on mean squared error" + "Name": "Średni błąd kwadratowy", + "Info": "Średni błąd kwadratowy to średnia kwadratów błędów.", + "Short": "Średni błąd kwadratowy", + "Title": "Dodatkowe informacje o średnim błędzie kwadratowym" }, "Precision": { - "Name": "Precision score", - "Info": "The precision is the ratio of true positives over all predicted positives.", - "Short": "Precision", - "Title": "Additional information on precision" + "Name": "Wynik precyzji", + "Info": "Precyzja to stosunek wyników prawdziwie dodatnich do wszystkich przewidywanych wyników dodatnich.", + "Short": "Precyzja", + "Title": "Dodatkowe informacje na temat precyzji" }, "Recall": { - "Name": "Recall score", - "Info": "The recall is the ratio of true positives over all actual positives.", - "Short": "Recall", - "Title": "Additional information on recall" + "Name": "Wynik kompletności", + "Info": "Kompletność to stosunek wyników prawdziwie dodatnich do wszystkich rzeczywistych wyników dodatnich.", + "Short": "Kompletność", + "Title": "Dodatkowe informacje na temat kompletności" }, "MacroPrecision": { - "Name": "Macro averaged precision score", - "Info": "The macro averaged precision is the ratio of true positives over all predicted positives computed independently per class and averaged.", - "Short": "Macro precision", - "Title": "Additional information on macro averaged precision" + "Name": "Wynik makrouśrednionej precyzji", + "Info": "Makrouśredniona precyzja to stosunek wyników prawdziwie dodatnich do wszystkich przewidywanych wyników dodatnich obliczany niezależnie na klasę i uśredniony.", + "Short": "Makroprecyzja", + "Title": "Dodatkowe informacje o makrouśrednionej precyzji" }, "MicroPrecision": { - "Name": "Micro averaged precision score", - "Info": "The micro averaged precision is the ratio of true positives over all predicted positives aggregated for all classes.", - "Short": "Micro precision", - "Title": "Additional information on micro averaged precision" + "Name": "Wynik mikrouśrednionej precyzji", + "Info": "Mikrouśredniona precyzja to stosunek wyników prawdziwie dodatnich do wszystkich przewidywanych wyników dodatnich zagregowanych dla wszystkich klas.", + "Short": "Mikroprecyzja", + "Title": "Dodatkowe informacje o mikrouśrednionej precyzji" }, "MacroRecall": { - "Name": "Macro averaged recall score", - "Info": "The macro averaged recall is the ratio of true positives over all actual positives computed independently per class and averaged.", - "Short": "Macro recall", - "Title": "Additional information on macro averaged recall" + "Name": "Wynik makrouśrednionej kompletności", + "Info": "Makrouśredniona kompletność to stosunek wyników prawdziwie dodatnich do wszystkich rzeczywistych wyników dodatnich obliczany niezależnie na klasę i uśredniony.", + "Short": "Makrokompletność", + "Title": "Dodatkowe informacje o makrouśrednionej kompletności" }, "MicroRecall": { - "Name": "Micro averaged recall score", - "Info": "The micro averaged recall is the ratio of true positives over all actual positives aggregated for all classes.", - "Short": "Micro recall", - "Title": "Additional information on micro averaged recall" + "Name": "Wynik mikrouśrednionej kompletności", + "Info": "Mikrouśredniona kompletność to stosunek wyników prawdziwie dodatnich do wszystkich rzeczywistych wyników dodatnich zagregowanych dla wszystkich klas.", + "Short": "Mikrokompletność", + "Title": "Dodatkowe informacje o mikrouśrednionej kompletności" }, "MacroF1Score": { - "Name": "Macro averaged F1 score", - "Info": "The macro averaged F1 score is the harmonic mean of the macro averaged precision and recall metrics.", - "Short": "Macro F1 score", - "Title": "Additional information on macro averaged F1 score" + "Name": "Średnia miara F1 (makro)", + "Info": "Średnia miara F1 (makro) jest średnią harmoniczną średniej makro precyzji i kompletności metryki.", + "Short": "Miara F1 (makro)", + "Title": "Dodatkowe informacje o średniej mierze F1 (makro)" }, "MicroF1Score": { - "Name": "Micro averaged F1 score", - "Info": "The micro averaged F1 score is the harmonic mean of the micro averaged precision and recall metrics.", - "Short": "Micro F1 score", - "Title": "Additional information on micro averaged F1 score" + "Name": "Średnia miara F1 (mikro)", + "Info": "Średnia miara F1 (mikro) jest średnią harmoniczną średniej mikro precyzji i kompletności metryki.", + "Short": "Miara F1 (mikro)", + "Title": "Dodatkowe informacje o średniej mierze F1 (mikro)" }, "MeanAveragePrecision": { - "Name": "Mean average precision score", - "Info": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall.", - "Short": "Mean avg precision", - "Title": "Additional information on mean average precision score" + "Name": "Wynik średniej precyzji", + "Info": "Średnia precyzja dla modeli wykrywania obiektów jest średnią wskaźnika średniej precyzja we wszystkich klasach. Daje to ocenę niezawodności modelu wykrywania obiektów i jednoczy kompromis między precyzją a kompletnością.", + "Short": "Średnia precyzja", + "Title": "Dodatkowe informacje o wyniku średniej precyzji" }, "AveragePrecision": { - "Name": "Average precision score", - "Info": "Average precision for object detection models is calculated for a selected class.", - "Short": "Avg precision", - "Title": "Additional information on average precision score" + "Name": "Wynik średniej precyzji", + "Info": "Średnia precyzja dla modeli wykrywania obiektów jest obliczana dla wybranej klasy.", + "Short": "Średnia precyzja", + "Title": "Dodatkowe informacje o wyniku średniej precyzji" }, "AverageRecall": { - "Name": "Average recall score", - "Info": "Average recall for object detection models is calculated for a selected class.", - "Short": "Avg recall", - "Title": "Additional information on average recall score" + "Name": "Wynik średniej kompletności", + "Info": "Średnia kompletność modeli wykrywania obiektów jest obliczana dla wybranej klasy.", + "Short": "Średnia kompletność", + "Title": "Dodatkowe informacje o wyniku średniej kompletności" }, "metricName": "Nazwa metryki", "metricValue": "Wartość metryki" }, "MetricSelector": { "selectorLabel": "Wybierz metrykę", - "feature1SelectorLabel": "Rows: Feature 1", - "feature2SelectorLabel": "Columns: Feature 2" + "feature1SelectorLabel": "Wiersze: funkcja 1", + "feature2SelectorLabel": "Kolumny: Funkcja 2" }, "Navigation": { "cohortSaved": "Zapisano nową kohortę! Zobacz listę kohort w ustawieniach kohorty.", @@ -433,9 +433,9 @@ "defaultLabelCopy": "Kopia wszystkich danych" }, "TreeView": { - "ariaLabel": "Interactive chart", - "disabledArialLabel": "Disabled interactive chart", - "treeMapInfoTitle": "Additional information on tree map", + "ariaLabel": "Wykres interakcyjny", + "disabledArialLabel": "Wyłączony wykres interakcyjny", + "treeMapInfoTitle": "Dodatkowe informacje na temat mapy drzewa", "treeDescription": "Wizualizacja drzewa wykorzystuje wzajemne informacje między każdą funkcją i błędem, aby najlepiej oddzielić w ramach danych błędne wystąpienia od wystąpień zakończonych powodzeniem w sposób hierarchiczny. Upraszcza to proces wykrywania i wyróżniania typowych wzorców niepowodzeń. Aby znaleźć ważne wzorce błędów, wyszukaj węzły oznaczone mocniejszym czerwonym kolorem (tzn. z wysokim wskaźnikiem błędów) i wyższą linią wypełnienia (tzn. z wysokim pokryciem błędów). Aby edytować listę funkcji używanych w drzewie, kliknij pozycję „Lista funkcji”. Użyj menu rozwijanego „wybierz metrykę”, aby dowiedzieć się więcej o błędach i wydajności węzłów zakończonych powodzeniem. Pamiętaj, że ten wybór metryki nie wpłynie na sposób generowania drzewa błędów.", "treeStaticDescription": "Wizualizacja drzewa wykorzystuje wzajemne informacje między każdą funkcją i błędem, aby najlepiej oddzielić w ramach danych błędne wystąpienia od wystąpień zakończonych powodzeniem w sposób hierarchiczny. Upraszcza to proces wykrywania i wyróżniania typowych wzorców niepowodzeń. Aby znaleźć ważne wzorce błędów, wyszukaj węzły oznaczone mocniejszym czerwonym kolorem (tzn. z wysokim wskaźnikiem błędów) i wyższą linią wypełnienia (tzn. z wysokim pokryciem błędów). Aby wyświetlić listę funkcji używanych podczas tworzenia drzewa błędów, kliknij pozycję „Lista funkcji”. Użyj menu rozwijanego „wybierz metrykę”, aby dowiedzieć się więcej o błędach i wydajności węzłów zakończonych powodzeniem. Pamiętaj, że ten wybór metryki nie wpłynie na sposób generowania drzewa błędów.", "disabledWarning": "Mapa drzewa błędów jest wyłączona, chyba że dla kohorty globalnej przełączono się w tryb reprezentowania wartości „Wszystkie dane”, ponieważ mapa drzewa jest generowana dla pełnego zestawu danych. Przełącz się z powrotem w tryb pełnego zestawu danych, aby wyświetlić mapę drzewa błędów." @@ -770,7 +770,7 @@ "countHelperText": "Histogram liczby punktów", "ditherLabel": "Powinny się wahać", "groupByCohort": "Grupuj według kohorty", - "logarithmicScaling": "Enable logarithmic scaling", + "logarithmicScaling": "Włączanie skalowania logarytmicznego", "numOfBins": "Liczba pojemników", "selectClass": "Wybierz klasę", "selectFeature": "Wybierz cechę", @@ -794,7 +794,7 @@ "importancePrefix": "Ważność", "numberOfDatapoints": "Liczba punktów danych", "rowIndex": "Indeks wiersza", - "absoluteIndex": "Absolute index", + "absoluteIndex": "Indeks bezwzględny", "xValue": "Wartość X", "yValue": "Wartość Y" }, @@ -822,12 +822,12 @@ }, "CohortEditor": { "columns": { - "index": "Index", - "dataset": "Dataset", - "predictedY": "Predicted Y", - "trueY": "True Y", - "classificationOutcome": "Classification outcome", - "regressionError": "Error" + "index": "Indeks", + "dataset": "Zestaw danych", + "predictedY": "Przewidywana wartość Y", + "trueY": "Prawda Y", + "classificationOutcome": "Wynik klasyfikacji", + "regressionError": "Błąd" }, "TreatAsCategorical": "Traktuj jako kategorialne", "addFilter": "Dodawanie filtru", @@ -852,8 +852,8 @@ "save": "Zapisz", "saveAndSwitch": "Zapisz i przełącz", "selectFilter": "Wybierz filtr", - "noFiltersApplied": "No filters applied", - "filterAdded": "Filter added" + "noFiltersApplied": "Nie zastosowano żadnych filtrów", + "filterAdded": "Dodano filtr" }, "Columns": { "classificationOutcome": "Wynik klasyfikacji", @@ -863,8 +863,8 @@ "falsePositive": "Wynik fałszywie dodatni", "none": "Liczba", "predictedProbabilities": "Prawdopodobieństwa przewidywania", - "predictedLabels": "Predicted labels", - "trueLabels": "True labels", + "predictedLabels": "Przewidywane etykiety", + "trueLabels": "Etykiety prawda", "regressionError": "Błąd regresji", "trueNegative": "Wynik prawdziwie ujemny", "truePositive": "Wynik prawdziwie dodatni", @@ -885,7 +885,7 @@ "aggregatePlots": "Zagreguj wykresy", "chartType": "Typ wykresu", "colorValue": "Wartość koloru", - "infoTitle": "Additional information on data analysis chart view", + "infoTitle": "Dodatkowe informacje na temat widoku wykresu analizy danych", "helperText": "Utwórz zestaw danych kohorty, aby analizować statystykę zestawu danych na filtrach, takich jak prognozowany wynik, funkcje zestawu danych i grupy błędów. Dowiedz się więcej o nadmiernej/niedostatecznej prezentacji w zestawie danych.", "individualDatapoints": "Poszczególne punkty danych", "missingParameters": "Ta karta wymaga dostarczenia zestawu danych oceny.", @@ -906,6 +906,8 @@ "index": "Indeks", "output": "Dane wyjściowe", "predictedY": "Przewidywana wartość Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Prawdopodobieństwo: {0}", "trueY": "Prawda Y", "xValue": "Wartość X:", @@ -974,10 +976,10 @@ "dependencePlotHelperText": "Ten wykres zależności pokazuje relacje wartości funkcji z odpowiadającymi jej wartościami istotności funkcji.", "dependencePlotTitle": "Wykresy zależności", "helperText": "Poznaj ważne funkcje typu top-k, które wpływają na ogólne przewidywania modelu (zwanego też objaśnieniem ogólnym). Użyj suwaka, aby wyświetlić istotności funkcji w kolejności malejącej. Wszystkie istotności funkcji kohort są wyświetlane obok siebie i można je wyłączyć, wybierając daną kohortę z legendy. Kliknij dowolną funkcję na wykresie, aby zobaczyć, w jaki sposób wartości wybranej funkcji wpływają na przewidywanie poniżej powierzchni gęstości.", - "infoTitle": "Additional information on aggregate feature importance", + "infoTitle": "Dodatkowe informacje na temat zagregowanej ważności funkcji", "legendHelpText": "Włącz lub wyłącz kohorty na wykresie, klikając elementy legendy.", "missingParameters": "Na tej karcie musi być podany parametr ważności cechy lokalnej.", - "sortByCohort": "Sort by cohort", + "sortByCohort": "Sortuj według kohorty", "sortBy": "Sortuj według punktu danych", "topAtoB": "Najlepsze {0} funkcji według ich istotności", "viewDependencePlotFor": "Wyświetl wykres zależności dla:", @@ -1020,15 +1022,15 @@ }, "Statistics": { "accuracy": "Dokładność: {0}", - "bleuScore": "Bleu score: {0}", - "bertScore": "Bert score: {0}", - "exactMatchRatio": "Exact match ratio: {0}", - "rougeScore": "Rouge Score: {0}", + "bleuScore": "Wynik Bleu: {0}", + "bertScore": "Wynik Berta: {0}", + "exactMatchRatio": "Dokładny współczynnik dopasowania: {0}", + "rougeScore": "Wynik Rouge: {0}", "fnr": "Wskaźnik wyników fałszywie ujemnych: {0}", "fpr": "Wskaźnik wyników fałszywie dodatnich: {0}", - "hammingScore": "Hamming score: {0}", + "hammingScore": "Wynik Hamminga: {0}", "meanPrediction": "Średnie przewidywanie {0}", - "meteorScore": "Meteor Score: {0}", + "meteorScore": "Wynik Meteor: {0}", "mse": "Średni błąd kwadratowy: {0}", "precision": "Precyzja: {0}", "rSquared": "R²: {0}", @@ -1036,10 +1038,10 @@ "selectionRate": "Współczynnik wyboru: {0}", "mae": "Średni błąd bezwzględny: {0}", "f1Score": "Miara F1: {0}", - "samples": "Sample size: {0}", - "meanAveragePrecision": "Mean average precision: {0}", - "averagePrecision": "Average precision: {0}", - "averageRecall": "Average recall: {0}" + "samples": "Rozmiar próbek: {0}", + "meanAveragePrecision": "Średnia precyzja: {0}", + "averagePrecision": "Średnia precyzja: {0}", + "averageRecall": "Średnia kompletność: {0}" }, "ValidationErrors": { "addFilters": "Dodaj filtry", @@ -1147,30 +1149,30 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Pulpit nawigacyjny możliwości interpretacji", - "importantWords": "Show most important words", + "importantWords": "Pokaż najważniejsze wyrazy", "topFeatureList": "Analiza listy najważniejszych funkcji", "allButton": "WSZYSTKIE FUNKCJE", "negButton": "CECHY NEGATYWNE", "posButton": "CECHY POZYTYWNE", - "legendText": "Positive scalar feature importances represent the extent that the words were important towards the classification of your selected label, and negative scalar feature importances represent words that encouraged your model away from your selected label.", - "legendTextForQA": "The left text box and the bar chart display the predictions of the model. The right text box shows the feature importance associated with a selected token. Positive feature importances represent the extent that the words were important towards marking the selected token as the starting/ending position of the answer.", + "legendText": "Dodatnia wartość skalarna istotności funkcji reprezentuje poziom, w jakim słowa były ważne dla klasyfikacji wybranej etykiety, a ujemna wartość skalarna istotności funkcji reprezentuje słowa, które odwiodły Twój model od wybranej etykiety.", + "legendTextForQA": "Lewe pole tekstowe i wykres słupkowy wyświetlają przewidywania modelu. W odpowiednim polu tekstowym jest wyświetlana ważność funkcji skojarzona z wybranym tokenem. Znaczenie cech pozytywnych reprezentuje zakres, w jakim wyrazy były ważne dla oznaczania wybranego tokenu jako pozycji początkowej/końcowej odpowiedzi.", "label": "Etykieta", "colon": ": ", - "startingPosition": "STARTING POSITION", - "endingPosition": "ENDING POSITION", - "predictedAnswer": "Predicted answer: ", - "trueAnswer": "True answer: ", - "inputs": "Inputs", - "outputs": "Outputs", - "sliderAriaLabel": "Slider for most important words" + "startingPosition": "POZYCJA POCZĄTKOWA", + "endingPosition": "POZYCJA KOŃCOWA", + "predictedAnswer": "Przewidywana odpowiedź: ", + "trueAnswer": "Prawdziwa odpowiedź: ", + "inputs": "Dane wejściowe", + "outputs": "Dane wyjściowe", + "sliderAriaLabel": "Suwak najważniejszych wyrazów" }, "Legend": { "featureLegend": "LEGENDA FUNKCJI TEKSTU", "posFeatureImportance": "WAŻNOŚĆ FUNKCJI POZYTYWNYCH", "negFeatureImportance": "NEGATYWNA WAŻNOŚĆ FUNKCJI", - "cls": "CLS: start of the sentence", - "sep": "SEP: end of the sentence", - "selectedWord": "Selected word: " + "cls": "CLS: początek zdania", + "sep": "SEP: koniec zdania", + "selectedWord": "Zaznaczony wyraz: " }, "BarChart": { "featureImportance": "WAŻNOŚĆ CECHY" @@ -1178,59 +1180,59 @@ }, "InterpretVision": { "Cohort": { - "close": "Close", - "errorCohortName": "Please choose a unique cohort name.", - "errorNumSelected": "Please select at least one (1) item.", - "itemsSelectedSingular": "item selected", - "itemsSelectedPlural": "items selected", - "save": "Save cohort", - "saveAndClose": "Save and close", - "saveAndSwitch": "Save and switch", - "textField": "New cohort name", - "title": "Save new cohort" + "close": "Zamknij", + "errorCohortName": "Wybierz unikatową nazwę kohorty.", + "errorNumSelected": "Wybierz co najmniej jeden (1) element.", + "itemsSelectedSingular": "wybrano element", + "itemsSelectedPlural": "wybrane elementy", + "save": "Zapisz kohortę", + "saveAndClose": "Zapisz i zamknij", + "saveAndSwitch": "Zapisz i przełącz", + "textField": "Nazwa nowej kohorty", + "title": "Zapisz nową kohortę" }, "Dashboard": { "allData": "Wszystkie dane", - "columnOne": "Image", + "columnOne": "Obraz", "columnTwo": "Indeks", "columnThree": "Prawda Y", "columnFour": "Przewidywana wartość Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Poprawne", + "columnFourOD": "Nieprawidłowe", "columnFive": "Inne metadane", - "chooseObject": "Choose a detected object", - "examples": "examples", + "chooseObject": "Wybierz wykryty obiekt", + "examples": "przykłady", "filter": "Filtr", - "indexLabel": "Image ", - "labelTypeDropdown": "Select label type", - "labelVisibilityDropdown": "Select labels to display", - "legendFailure": "failure", - "legendSuccess": "success", - "loading": "Computing explanation for index", - "multiselect": "Multiselect", - "notdefined": "object scenario not defined", - "objectSelect": "Object Selection", + "indexLabel": "Obraz ", + "labelTypeDropdown": "Wybierz typ etykiety", + "labelVisibilityDropdown": "Wybierz etykiety do wyświetlenia", + "legendFailure": "niepowodzenie", + "legendSuccess": "sukces", + "loading": "Obliczanie objaśnienia dla indeksu", + "multiselect": "Wybór wielokrotny", + "notdefined": "nie zdefiniowano scenariusza obiektu", + "objectSelect": "Wybór obiektu", "pageSize": "Rozmiar strony: ", - "panelTitle": "Selected instance", - "panelExplanation": "Explanation", - "panelInformation": "Information", - "predictedLabel": "Predicted label", - "predictedY": "Predicted: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", - "prefix": "Object: ", - "rows": "Rows: ", + "panelTitle": "Wybrane wystąpienie", + "panelExplanation": "Wyjaśnienie", + "panelInformation": "Informacje", + "predictedLabel": "Przewidywana etykieta", + "predictedY": "Prognozowana: ", + "correctDetections": "Poprawne wykrycia: ", + "incorrectDetections": "Nieprawidłowe wykrycia: ", + "prefix": "Obiekt: ", + "rows": "Wiersze: ", "search": "Wyszukaj", - "selectAll": "Select all", + "selectAll": "Wybierz wszystkie", "settings": "Ustawienia", - "showAll": "Show all", + "showAll": "Pokaż wszystko", "tabOptionFirst": "Widok eksploratora obrazów", "tabOptionSecond": "Widok tabeli", - "tabOptionThird": "Class view", + "tabOptionThird": "Widok klas", "thumbnailSize": "Rozmiar miniatury", "titleBarError": "Wystąpienia błędów", "titleBarSuccess": "Wystąpienia sukcesu", - "trueY": "Ground truth: " + "trueY": "Walidacja oparta na bezpośredniej obserwacji (dane empiryczne): " } }, "ModelAssessment": { @@ -1239,15 +1241,15 @@ "CalloutContent": "Dodanie niektórych składników (widok drzewa błędów, mapa cieplna błędów) umożliwi Ci filtrowanie danych z kohorty globalnej, które są widoczne w składnikach poniżej.", "CalloutTitle": "Dodaj składnik", "TabAddedMessage": { - "DataAnalysis": "Data analysis component added", - "FeatureImportances": "Feature importances component added", - "ErrorAnalysis": "Error analysis component added", - "Fairness": "Fairness component added", - "ModelOverview": "Model overview component added", - "CausalAnalysis": "Causal analysis component added", - "Counterfactuals": "Counterfactuals component added", - "Vision": "Vision data explorer component added", - "Forecasting": "Forecasting what-if component added" + "DataAnalysis": "Dodano składnik analizy danych", + "FeatureImportances": "Dodano składnik ważności funkcji", + "ErrorAnalysis": "Dodano składnik analizy błędów", + "Fairness": "Dodano składnik uczciwości", + "ModelOverview": "Dodano składnik przeglądu modelu", + "CausalAnalysis": "Dodano składnik analizy przyczynowej", + "Counterfactuals": "Dodano składnik hipotezy przeciwnej", + "Vision": "Dodano składnik eksploratora danych usługi Azure Cognitive Service dla wizji", + "Forecasting": "Dodano składnik analizy warunkowej prognozowania" } }, "CausalAnalysis": { @@ -1275,7 +1277,7 @@ }, "CohortInformation": { "ShiftCohort": "Przełącz kohortę", - "SwitchTimeSeries": "Switch time series", + "SwitchTimeSeries": "Przełączanie szeregów czasowych", "NewCohort": "Nowa kohorta", "DataPoints": "Liczba punktów danych", "DefaultCohort": " (wartość domyślna)", @@ -1287,7 +1289,7 @@ "CohortSettingsTitle": "Ustawienia kohorty" }, "ComponentNames": { - "ChartView": "Chart view", + "ChartView": "Widok wykresu", "CausalAnalysis": "Analiza przyczyn", "Counterfactuals": "Hipotezy przeciwne", "DataAnalysis": "Analiza danych", @@ -1296,10 +1298,10 @@ "ErrorAnalysis": "Analiza błędów", "Fairness": "Uczciwość", "FeatureImportances": "Istotność funkcji", - "Forecasting": "Forecasting", + "Forecasting": "Prognozowanie", "ModelOverview": "Przegląd modelu", - "TableView": "Table view", - "VisionTab": "Vision data explorer" + "TableView": "Widok tabeli", + "VisionTab": "Eksplorator danych usługi Azure Cognitive Service dla wizji" }, "DashboardSettings": { "Content": "Lista pokazuje układ pulpitu nawigacyjnego. Możesz przefiltrować dane za pomocą składnika analizy błędów, który ma być wyświetlany w poniższych składnikach.", @@ -1458,16 +1460,16 @@ "GlobalExplanation": "Zagregowana istotność funkcji", "IncorrectPredictions": "Niepoprawne przewidywania", "InfoTitle": "Additional information on feature importance values", - "IndividualFeatureTabular": "Select a datapoint by clicking on a datapoint (up to 5 datapoints) in the table to view their local feature importance values (local explanation) and individual conditional expectation (ICE) plots.", + "IndividualFeatureTabular": "Wybierz punkt danych, klikając punkt danych (maksymalnie 5 punktów danych) w tabeli, aby wyświetlić poniżej wykresy przedstawiające ich lokalne wartości istotności funkcji (lokalne objaśnienie) oraz indywidualne oczekiwanie warunkowe (ICE).", "IndividualFeatureText": "Select a datapoint by clicking on a datapoint in the table to view the local feature importance values (local explanation) from SHAP's text explainer.", "LocalExplanation": "Indywidualna istotność funkcji", "SelectionCounter": "wybrane punkty danych {0}/{1}", "SelectionLimit": "W tej chwili można wybrać maksymalnie 5 punktów danych.", - "RowCheckboxAriaLabel": "Row checkbox", - "SelectionColumnAriaLabel": "Toggle selection" + "RowCheckboxAriaLabel": "Pole wyboru wiersza", + "SelectionColumnAriaLabel": "Przełącz wybór" }, "IndividualFeatureImportanceView": { - "SmallInstanceSelection": "Instance selection" + "SmallInstanceSelection": "Wybór wystąpienia" }, "MainMenu": { "DashboardSettings": "Konfiguracja pulpitu nawigacyjnego", @@ -1483,44 +1485,44 @@ "ModelOverview": { "metrics": { "accuracy": { - "name": "Accuracy score", + "name": "Ocena dokładności", "description": "Ułamek punktów danych sklasyfikowanych poprawnie." }, "exactMatchRatio": { - "name": "Exact match ratio", - "description": "The ratio of instances classified correctly for every label." + "name": "Dokładny współczynnik dopasowania", + "description": "Współczynnik wystąpień sklasyfikowanych poprawnie dla każdej etykiety." }, "meteorScore": { - "name": "Meteor Score", - "description": "METEOR Score is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision in question answering task." + "name": "Wynik Meteor", + "description": "Wynik METEOR jest obliczany na podstawie harmonicznej średniej precyzji i kompletności, gdzie waga kompletności jest większa niż precyzji w zadaniu odpowiadania na pytania." }, "bleuScore": { - "name": "Bleu Score", - "description": "Bleu Score measures the ratio of words (and/or n-grams) in the machine generated text that appeared in the reference text in question answering task." + "name": "Wynik Bleu", + "description": "Wskaźnik Bleu mierzy stosunek wyrazów (i/lub n-gramów) w tekście wygenerowanym maszynowo, który pojawił się w tekście referencyjnym w zadaniu odpowiadania na pytanie." }, "bertScore": { - "name": "Bert Score", - "description": "BERTScore focuses on computing semantic similarity between tokens of reference and machine generated text in question answering task." + "name": "Wynik Berta", + "description": "Wskaźnik BERTScore koncentruje się na przetwarzaniu semantycznego podobieństwa między tokenami odwołania i tekstem wygenerowanym maszynowo w zadaniu odpowiadania na pytanie." }, "rougeScore": { - "name": "Rouge Score", - "description": "Rouge Score measures the ratio of words (and/or n-grams) in the reference text that appeared in the machine generated text in question answering task." + "name": "Wynik Rouge", + "description": "Wynik Rouge mierzy stosunek wyrazów (i/lub n-gramów) w tekście referencyjnym, który pojawił się w tekście wygenerowanym maszynowo w zadaniu odpowiadania na pytania." }, "hammingScore": { - "name": "Hamming score", - "description": "The average ratio of labels classified correctly among those classified as 1 in multilabel task." + "name": "Wynik Hamminga", + "description": "Średni stosunek etykiet sklasyfikowanych poprawnie wśród tych sklasyfikowanych jako 1 w zadaniu wielu etykiet." }, "f1Score": { "name": "Miara F1", "description": "Miara F1 to średnia harmoniczna precyzji i czułości." }, "f1ScoreMacro": { - "name": "Macro F1 score", - "description": "Macro F1 score is the harmonic mean of precision and recall for each class, with each class weighted equally." + "name": "Miara F1 (makro)", + "description": "Wynik makra F1 jest harmoniczną średnią precyzji i kompletności dla każdej klasy, przy założeniu, że każda klasa ma jednakową wagę." }, "f1ScoreMicro": { - "name": "Micro F1 score", - "description": "Micro F1 score is the harmonic mean of precision and recall for each class, with each class weighted according to how many instances it contains." + "name": "Miara F1 (mikro)", + "description": "Wskaźnik Micro F1 jest harmoniczną średnią precyzji i kompletności dla każdej klasy, a każda klasa jest ważona zgodnie z liczbą wystąpień, które zawiera." }, "meanAbsoluteError": { "name": "Średni błąd bezwzględny", @@ -1535,24 +1537,24 @@ "description": "Ułamek punktów danych sklasyfikowanych poprawnie wśród tych, które sklasyfikowano jako 1." }, "precisionMacro": { - "name": "Macro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted equally." + "name": "Wynik makroprecyzji", + "description": "Ułamek punktów danych sklasyfikowanych poprawnie wśród tych sklasyfikowanych jako 1 dla każdej klasy, przy założeniu, że każda klasa ma jednakową wagę." }, "precisionMicro": { - "name": "Micro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted according to how many instances it contains." + "name": "Wskaźnik mikroprecyzji", + "description": "Ułamek punktów danych sklasyfikowanych poprawnie wśród tych sklasyfikowanych jako 1 dla każdej klasy, gdzie klasa jest ważona zgodnie z liczbą wystąpień, które zawiera." }, "recall": { "name": "Wynik kompletności", "description": "Ułamek punktów danych sklasyfikowanych poprawnie wśród tych, których rzeczywista etykieta to 1. Alternatywne nazwy: rzeczywisty współczynnik poprawności, wrażliwość." }, "recallMacro": { - "name": "Macro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted equally." + "name": "Wynik kompletności makr", + "description": "Ułamek punktów danych sklasyfikowanych poprawnie wśród tych, których prawdziwa etykieta wynosi 1 dla każdej klasy, przy założeniu, że każda klasa ma jednakową wagę." }, "recallMicro": { - "name": "Micro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted according to how many instances it contains." + "name": "Wskaźnik mikrokompletności", + "description": "Ułamek punktów danych sklasyfikowanych poprawnie wśród tych, których prawdziwa etykieta wynosi 1 dla każdej klasy, a każda klasa jest ważona zgodnie z liczbą wystąpień, które zawiera." }, "falsePositiveRate": { "name": "Wskaźnik wyników fałszywie dodatnich", @@ -1571,32 +1573,32 @@ "description": "Średnia wszystkich przewidywań." }, "meanAveragePrecision": { - "name": "Mean Average Precision score", - "description": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall." + "name": "Wynik średniej precyzji", + "description": "Średnia precyzja dla modeli wykrywania obiektów jest średnią wskaźnika średniej precyzja we wszystkich klasach. Daje to ocenę niezawodności modelu wykrywania obiektów i jednoczy kompromis między precyzją a kompletnością." }, "averagePrecision": { - "name": "Average Precision score", - "description": "Average precision for object detection models is calculated for a selected class." + "name": "Średni wynik dokładności", + "description": "Średnia precyzja dla modeli wykrywania obiektów jest obliczana dla wybranej klasy." }, "averageRecall": { - "name": "Average Recall score", - "description": "Average recall for object detection models is calculated for a selected class." + "name": "Średni wynik kompletności", + "description": "Średnia kompletność modeli wykrywania obiektów jest obliczana dla wybranej klasy." }, "fairnessMetricDifference": "Różnica", "fairnessMetricRatio": "Współczynnik" }, "metricsDropdown": "Metryki", - "metricsTypeDropdown": "Aggregate method", + "metricsTypeDropdown": "Metoda agregująca", "metricTypes": { - "macro": "Macro", - "micro": "Micro" + "macro": "Makro", + "micro": "Mikro" }, - "classSelectionDropdown": "Select class(es)", + "classSelectionDropdown": "Wybierz klasy", "iouThresholdDropdown": { - "name": "IoU Threshold", - "description": "Intersection over Union quantifies the degree of overlap between the prediction and ground truth bounding box of a detected object in an image. For example, setting an IoU threshold of 70% means that a prediction with greater than 70% overlap with ground truth is True, thus influencing the definition of prediction correctness and calculation of other performance metrics.", + "name": "Próg IOU", + "description": "Część wspólna sumy zbiorów określa stopień nakładania się prognozy i pola ograniczenia danych empirycznych dla wykrytego obiektu na obrazie. Na przykład ustawienie progu IoU równego 70% oznacza, że prognoza z wartością nałożenia się na wartość danych empirycznych większą niż 70% oznacza Prawdę. To natomiast wpływa na definicję poprawności przewidywania i obliczanie innych metryk wydajności.", "iconId": "iouThresholdIconId", - "title": "Learn about the IoU threshold" + "title": "Dowiedz się więcej o progu IoU" }, "notAvailable": "Nie dotyczy", "countColumnHeader": "Rozmiar próbki", @@ -1608,14 +1610,14 @@ "featuresDropdown": "Funkcja(e)", "metricChartDropdownSelectionHeader": "Metryka", "probabilityForClassSelectionHeader": "Prawdopodobieństwo dla klasy", - "targetSelectionHeader": "Target", + "targetSelectionHeader": "Element docelowy", "metricSelectionDropdownPlaceholder": "Wybierz metryki, aby porównać kohorty.", - "classSelectionDropdownPlaceholder": "Select class name for class-based analysis.", + "classSelectionDropdownPlaceholder": "Wybierz nazwę klasy na potrzeby analizy opartej na klasie.", "featureSelectionDropdownPlaceholder": "Wybierz funkcje do użycia na potrzeby analizy opartej na funkcjach.", "probabilityDistributionPivotItem": "Rozkład prawdopodobieństwa", - "regressionDistributionPivotItem": "Target distribution", + "regressionDistributionPivotItem": "Dystrybucja docelowa", "metricsVisualizationsPivotItem": "Wizualizacje metryk", - "confusionMatrixPivotItem": "Confusion matrix", + "confusionMatrixPivotItem": "Macierz błędów", "disaggregatedAnalysisFeatureSelectionPlaceholder": "Wybierz funkcje, aby wygenerować analizę opartą na funkcjach.", "tableCountTooltip": "Kohorta {0} zawiera wystąpienia {1}.", "tableMetricTooltip": "{0} modelu w kohorcie {1} jest {2}", @@ -1626,36 +1628,36 @@ "metricSelectionButton": "Wybierz metrykę", "cohortSelectionButton": "Wybierz kohorty", "probabilityLabelSelectionButton": "Wybierz etykietę", - "regressionTargetSelectionButton": "Choose target", + "regressionTargetSelectionButton": "Wybierz cel", "selectAllCohortsOption": "Wybierz wszystkie", "other": "Inne", "BoxPlot": { "outlierProbability": "prawdopodobieństwo", "outlierLabel": "Elementy odstające", "boxPlotSeriesLabel": "Wykres skrzynkowy", - "lowerWhisker": "Lower whisker", - "upperWhisker": "Upper whisker", - "median": "Median", - "lowerQuartile": "Lower quartile", - "upperQuartile": "Upper quartile" + "lowerWhisker": "Dolny wąs", + "upperWhisker": "Górny wąs", + "median": "Mediana", + "lowerQuartile": "Kwartyl dolny", + "upperQuartile": "Kwartyl górny" }, "chartConfigApply": "Zastosuj", "chartConfigCancel": "Anuluj", "chartConfigDatasetCohortSelectionPlaceholder": "Wybierz kohorty zestawów danych", "chartConfigFeatureBasedCohortSelectionPlaceholder": "Wybierz kohorty oparte na funkcjach", "confusionMatrix": { - "confusionMatrixCohortSelectionLabel": "Select dataset cohort", - "confusionMatrixClassSelectionLabel": "Select classes", - "confusionMatrixClassMinSelectionError": "Select at least {0} classes to visualize the confusion matrix.", - "confusionMatrixClassMaxSelectionError": "Select at most {0} classes to visualize the confusion matrix.", - "confusionMatrixClassSelectionDefaultPlaceholder": "Choose classes", - "confusionMatrixHeatmapTooltip": "{0} datapoints should be {1}, predicted to be {2}", - "confusionMatrixYAxisLabel": "True Class", - "confusionMatrixXAxisLabel": "Predicted Class", - "class": "Class" + "confusionMatrixCohortSelectionLabel": "Wybierz kohorty zestawów danych", + "confusionMatrixClassSelectionLabel": "Wybierz klasy", + "confusionMatrixClassMinSelectionError": "Wybierz co najmniej {0} klas(y), aby zwizualizować macierz błędów.", + "confusionMatrixClassMaxSelectionError": "Wybierz co najwyżej {0} klas(y), aby zwizualizować macierz błędów.", + "confusionMatrixClassSelectionDefaultPlaceholder": "Wybierz klasy", + "confusionMatrixHeatmapTooltip": "{0} punkty danych powinny mieć wartość {1}, przewidywana wartość to {2}", + "confusionMatrixYAxisLabel": "Prawdziwa klasa", + "confusionMatrixXAxisLabel": "Przewidywana klasa", + "class": "Klasa" }, "nA": "Nie dotyczy", - "disaggregatedAnalysisBaseCohortDisclaimer": "The cohorts in the following feature-based analysis are based on the global cohort, {0}.", + "disaggregatedAnalysisBaseCohortDisclaimer": "Kohorty w następującej analizie opartej na funkcjach są oparte na kohorcie globalnej, {0}.", "disaggregatedAnalysisBaseCohortWarning": "W odróżnieniu od kohorty {0}, {1} zawiera filtry. W rezultacie przechwytuje tylko podzbiór całego zestawu danych, a szczegółowe informacje mogą nie być uogólniane do pełnego zestawu danych.", "probabilitySplineChartToggleLabel": "Użyj wykresu krzywej dowolnego kształtu", "countAxisLabel": "Liczba", @@ -1685,76 +1687,76 @@ "flyoutDescription": "Możesz wyświetlić kohorty zestawów danych lub kohorty funkcji. Jeśli kohorty funkcji są niedostępne, musisz najpierw wybrać co najmniej jedną funkcję w widoku kohort funkcji. Następnie są generowane kohorty funkcji i możesz je wybrać tutaj." }, "regressionTargetOptions": { - "predictedY": "Predicted Y", - "trueY": "True Y", - "error": "Error" + "predictedY": "Przewidywana wartość Y", + "trueY": "Prawda Y", + "error": "Błąd" }, "topLevelDescription": "Oceń wydajność modelu, badając rozkład wartości przewidywania i wartości metryk wydajności modelu. Użyj karty „Kohorty zestawów danych”, aby zbadać model, patrząc na analizę porównawczą jego wydajności w różnych wstępnie utworzonych lub nowo utworzonych kohortach zestawów danych. Użyj „Kohorty funkcji”, aby zbadać model, patrząc na analizę porównawczą jego wydajności w podkohortach funkcji wrażliwych/niewrażliwych. (np. wydajność dla różnych płci, poziomy dochodów).", - "infoTitle": "Additional information on model overview", + "infoTitle": "Dodatkowe informacje na temat przeglądu modelu", "visualDisplayToggleLabel": "Pokaż mapę cieplną", "featureBasedViewDescription": "Wybierz maksymalnie dwie funkcje, aby wyświetlić podział wydajności modelu dla kohort opartych na funkcjach (jeśli wybrano jedną funkcję) lub kohorty wspólnej (jeśli zostaną wybrane dwie funkcje)." }, "TableViewTab": { - "Heading": "View the dataset in a table format for all features and rows." + "Heading": "Wyświetl zestaw danych w formacie tabeli dla wszystkich funkcji i wierszy." } }, "Forecasting": { - "target": "Target", - "whatIfForecastingHeader": "What-if analysis", - "forecastHeader": "Forecast analysis", - "whatIfForecastingDescription": "What-if allows you to perturb features for your entire time series and observe how the model's forecast changes.", - "whatIfForecastingChooseTimeSeries": "To start, choose a time series from the options below.", - "forecastDescription": "Forecast analysis compares your model's forecast to the actual values of your time series. To enable what-if analysis, provide a dataset with features.", - "timeSeries": "Time series", - "selectTimeSeries": "Select a time series.", - "singleTimeSeries": "The dataset contains only a single time series '{0}' which has been selected by default.", - "trueY": "True Y", - "baselinePrediction": "Baseline prediction", - "forecastComparisonHeader": "Compare What-if Forecasts", - "forecastComparisonChartTitle": "Forecasts", - "forecastComparisonChartTimeAxisLabel": "Time", + "target": "Element docelowy", + "whatIfForecastingHeader": "Analiza warunkowa", + "forecastHeader": "Analiza prognozy", + "whatIfForecastingDescription": "Funkcja analizy warunkowej umożliwia zakłócanie funkcji dla całego szeregu czasowego i obserwowanie zmian prognozy modelu.", + "whatIfForecastingChooseTimeSeries": "Aby rozpocząć, wybierz szereg czasowy z poniższych opcji.", + "forecastDescription": "Analiza prognozy porównuje prognozę modelu z rzeczywistymi wartościami szeregów czasowych. Aby włączyć analizę warunkową, udostępnij zestaw danych z funkcjami.", + "timeSeries": "Szereg czasowy", + "selectTimeSeries": "Wybierz szereg czasowy.", + "singleTimeSeries": "Zestaw danych zawiera tylko jedną serię czasową „{0}”, która została wybrana domyślnie.", + "trueY": "Prawda Y", + "baselinePrediction": "Przewidywanie punktu odniesienia", + "forecastComparisonHeader": "Porównanie prognoz analizy warunkowej", + "forecastComparisonChartTitle": "Prognozy", + "forecastComparisonChartTimeAxisLabel": "Czas", "Transformations": { - "multiply": "multiply", - "divide": "divide", - "add": "add", - "subtract": "subtract", - "change": "change to" + "multiply": "mnożenie", + "divide": "podzielić", + "add": "dodaj", + "subtract": "odejmowanie", + "change": "zmień na" }, "TransformationCreation": { - "title": "Create what-if scenario", - "nameLabel": "What-if scenario name", - "featureInstructions": "Choose a feature to perturb.", - "operationInstructions": "Choose an operation to apply to the feature.", - "operationDropdownHeader": "Operation", - "featureDropdownHeader": "Feature", - "valueSpinButtonHeader": "Value", - "scenarioNamingInstructionsPlaceholder": "Enter a unique name", - "scenarioNamingInstructions": "Enter a name for your what-if scenario.", - "scenarioNamingCollisionMessage": "This name exists already. Please enter a unique name.", - "scenarioNamingLengthMessage": "The name must be between 1 and 50 characters. The actual length is {0}.", - "scenarioNamingInvalidCharactersMessage": "The name can only contain alphanumeric characters, whitespaces, dashes, or underscores, and needs to start with an alphanumeric character.", - "valueErrorMessage": "For operation {0} please select a value other than {1}.", - "invalidCombinationErrorMessage": "This is identical to an existing what-if scenario. Please change the feature, operation, or value.", - "addTransformationButton": "Add Transformation", - "divisionAndMultiplicationBy": "by" + "title": "Tworzenie scenariusza analizy warunkowej", + "nameLabel": "Nazwa scenariusza analizy warunkowej", + "featureInstructions": "Wybierz funkcję, którą chcesz zakłócić.", + "operationInstructions": "Wybierz operację, która ma być zastosowana do funkcji.", + "operationDropdownHeader": "Operacja", + "featureDropdownHeader": "Funkcja", + "valueSpinButtonHeader": "Wartość", + "scenarioNamingInstructionsPlaceholder": "Podaj unikatową nazwę", + "scenarioNamingInstructions": "Wprowadź nazwę scenariusza analizy warunkowej.", + "scenarioNamingCollisionMessage": "Ta nazwa już istnieje. Wprowadź unikatową nazwę.", + "scenarioNamingLengthMessage": "Nazwa musi zawierać od 1 do 50 znaków. Rzeczywista długość to {0}.", + "scenarioNamingInvalidCharactersMessage": "Nazwa może zawierać tylko znaki alfanumeryczne, białe znaki, łączniki lub podkreślenia i musi zaczynać się od znaku alfanumerycznego.", + "valueErrorMessage": "Aby wykonać operację {0}, wybierz wartość inną niż {1}.", + "invalidCombinationErrorMessage": "Jest to identyczne w stosunku do istniejącego scenariusza analizy warunkowej. Zmień funkcję, operację lub wartość.", + "addTransformationButton": "Dodaj przekształcenie", + "divisionAndMultiplicationBy": "przez" }, "TransformationTable": { - "nameColumnHeader": "Name", - "methodColumnHeader": "Method", - "divisionAndMultiplicationBy": "by ", - "header": "What-if Forecasts ({0})" + "nameColumnHeader": "Nazwa", + "methodColumnHeader": "Metoda", + "divisionAndMultiplicationBy": "przez ", + "header": "Prognozy warunkowe ({0})" }, "TimeSeries": { - "apply": "Apply", - "cancel": "Cancel", - "cohortList": "Time series list", - "selectCohort": "Select a time series", - "shiftCohort": "Switch time series", - "shiftCohortDescription": "Select a time series from the time series list. Apply the time series to the dashboard." + "apply": "Zastosuj", + "cancel": "Anuluj", + "cohortList": "Lista szeregów czasowych", + "selectCohort": "Wybierz szereg czasowy", + "shiftCohort": "Przełączanie szeregów czasowych", + "shiftCohortDescription": "Wybierz szereg czasowy z listy szeregów czasowych. Zastosuj szeregi czasowe do pulpitu nawigacyjnego." }, "TimeSeriesSettings": { - "CohortSettingsDescription": "Time series are pre-defined based on time series identifying columns.", - "CohortSettingsTitle": "Time series settings" + "CohortSettingsDescription": "Szeregi czasowe są wstępnie zdefiniowane na podstawie szeregów czasowych identyfikujących kolumny.", + "CohortSettingsTitle": "Ustawienia szeregów czasowych" } } } \ No newline at end of file diff --git a/libs/localization/src/lib/en.pt-BR.json b/libs/localization/src/lib/en.pt-BR.json index 5f8c5db58c..13c4b0d8e5 100644 --- a/libs/localization/src/lib/en.pt-BR.json +++ b/libs/localization/src/lib/en.pt-BR.json @@ -3,26 +3,26 @@ "close": "Fechar", "tooltipButton": "Botão dica de ferramenta", "identityFeature": "Recurso de identidade", - "infoTitle": "Additional information", - "spinButton": "Spin", - "editButton": "Edit", - "decreaseValue": "Decrease value", - "increaseValue": "Increase value", - "decreaseValueByOne": "Decrease value by 1", - "increaseValueByOne": "Increase value by 1", - "loading": "Loading..." + "infoTitle": "Informações adicionais", + "spinButton": "Girar", + "editButton": "Editar", + "decreaseValue": "Diminuir o valor", + "increaseValue": "Aumentar o valor", + "decreaseValueByOne": "Diminuir valor em 1", + "increaseValueByOne": "Aumentar o valor em 1", + "loading": "Carregando..." }, "ChartContextMenu": { - "hideData": "Hide data table", - "viewData": "View data table", - "viewInFullScreen": "View in full screen", - "printChart": "Print chart", - "downloadCSV": "Download CSV", - "downloadPNG": "Download PNG image", - "downloadJPEG": "Download JPEG image", - "downloadPDF": "Download PDF document", - "downloadSVG": "Download SVG vector image", - "downloadXLS": "Download XLS" + "hideData": "Ocultar tabela de dados", + "viewData": "Exibir tabela de dados", + "viewInFullScreen": "Exibir em tela inteira...", + "printChart": "Imprimir Gráfico", + "downloadCSV": "Baixar CSV", + "downloadPNG": "Baixar imagem PNG", + "downloadJPEG": "Baixar imagem JPEG", + "downloadPDF": "Baixar documento PDF", + "downloadSVG": "Baixar imagem de vetor SVG", + "downloadXLS": "Baixar XLS" }, "CausalAnalysis": { "AggregateView": { @@ -39,7 +39,7 @@ "description": "A análise causal responde a perguntas do tipo \"what if\" sobre como os resultados do mundo real teriam mudado sob diferentes escolhas de políticas, como uma estratégia de preços diferente para um produto ou um tratamento alternativo para um paciente. Ao contrário das previsões do modelo que identificam padrões de correlação importantes, essas ferramentas ajudam a identificar os recursos causais mais importantes que afetam diretamente o resultado de seu interesse. Esses modelos identificam o efeito causal de um recurso (normalmente referido como um “tratamento”), mantendo outros recursos confusos constantes. Para obter melhores resultados, certifique-se de que o conjunto de dados completo contém todos os recursos disponíveis que podem se correlacionar com o resultado como fatores de confundidores.", "directAggregate": "Efeito causal direto agregado de cada tratamento com intervalo de confiança de 95%.", "here": "aqui", - "infoTitle": "Additional information on aggregated causal effects", + "infoTitle": "Mais informações sobre efeitos causais agregados", "lasso": "Um laço (ou regressão logística se y for binário) foi adequado para prever y a partir de X[-i], e um laço (ou regressão logística se X[i] for categórico) foi adequado para prever X[i] de Χ[-i]. O efeito causal pode ser visto como a correlação média da variação residual/surpresa das duas tarefas de previsão. Saiba mais sobre Double Machine Learning", "unconfounding": "O que são recursos confusos?" }, @@ -51,7 +51,7 @@ "description": "Os efeitos causais individuais podem informar intervenções personalizadas, como uma promoção direcionada aos clientes ou um plano de tratamento individualizado. Como um indivíduo com um determinado conjunto de recursos responderia a uma alteração em um recurso causal ou tratamento? A ferramenta what-if causal calcula alterações de margens nos resultados do mundo real para um determinado indivíduo se você alterar o nível de um tratamento. Essa análise permite que você entenda como os resultados do mundo real seriam alterados em diferentes opções de políticas, como uma estratégia de preços diferente para um produto ou um tratamento alternativo para um paciente. Especifique o tratamento de interesse e observe como o resultado no mundo real seria alterado.", "directIndividual": "Efeito causal individual direto de cada tratamento com intervalo de confiança de 95%.", "index": "Índice DataPoint", - "infoTitle": "Additional information on individual causal what-if", + "infoTitle": "Informações adicionais sobre o what-if causal individual", "missingParameters": "Esta guia requer que um conjunto de dados de avaliação seja fornecido.", "newOutcome": "Novos resultados", "selectTreatment": "Selecionar o tratamento", @@ -85,7 +85,7 @@ "averageGainBinary": "Ganhos médios ao definir o tratamento {0} para seu valor de linha de base {1}.", "averageGainContinuous": "Ganhos médios de políticas alternativas em relação a nenhum tratamento “{0}”.", "header": "Essas ferramentas ajudam a construir políticas para futuras intervenções. Você pode identificar quais partes de sua amostra experimentam as maiores respostas a alterações nos recursos causais, ou tratamentos, e construir regras para definir quais populações futuras devem ser direcionadas para intervenções específicas.", - "infoTitle": "Additional information on treatment policy", + "infoTitle": "Mais informações sobre a política de tratamento", "nSample": "n = {0}", "noData": "Sem dados" } @@ -116,8 +116,8 @@ "cancel": "Cancelar", "title": "Alternar Coorte", "subText": "Selecione uma coorte da lista de coortes. Aplique a coorte ao painel.", - "selectCohort": "Select a cohort", - "cohortList": "Cohort list" + "selectCohort": "Selecione uma coorte", + "cohortList": "Lista de coorte" }, "PreBuiltCohort": { "featureNameNotFound": "Nome do recurso não encontrado no conjunto de dados", @@ -148,13 +148,13 @@ "predictedClass": "Classe prevista", "predictedValue": "Valor previsto" }, - "Size": "Size", - "loading": "Loading...", + "Size": "Tamanho", + "loading": "Carregando...", "counterfactualEx": "Ex Contrafactual {0}", "counterfactualName": "Nome contrafactual de hipótese", "createWhatIfCounterfactual": "Criar contrafactual de hipótese", "createCounterfactual": "Contrafactual", - "revertToBubbleChart": "View bubble chart", + "revertToBubbleChart": "Exibir gráfico de bolhas", "createOwn": "Crie seu próprio contrafactual:", "currentClass": "Classe atual", "currentRange": "Intervalo atual", @@ -167,9 +167,9 @@ "listDescription": "Essa lista mostra quais pontos de dados na amostra de dados atual têm a maior resposta causal ao tratamento selecionado, com base em todos os recursos incluídos no modelo causal estimado. As cinco colunas da esquerda relatam se o tratamento é recomendado para a observação, o tratamento atual, o efeito estimado do tratamento (efeito de aplicar um tratamento de uma linha de base sem tratamento para tratamentos binários ou aumentar/diminuir o recurso de tratamento em 10% do tamanho de tratamento típico na amostra: [dinâmico: relatar a alteração numérica no tratamento que usamos] ), e os intervalos de confiança inferior e superior (IC) para este efeito. As colunas restantes mostram o status do tratamento atual e outras características de cada observação.", "localImportanceDescription": "Os recursos mais bem classificados na Linha {0} para perturbar a realização da previsão do modelo desejado. Com base na análise de what-if para previsão: {1}", "localImportanceSelectData": "Selecione um ponto de dados para exibir o gráfico de importância local", - "largeLocalImportanceSelectData": "Select a bubble, followed by a data point to view local importance chart", - "localImportanceFetchError": "There was an error while fetching the local importance data. Error details: {0} Please check the data used.", - "BubbleChartFetchError": "There was an error while fetching the data. Error details: {0} Please check the data used.", + "largeLocalImportanceSelectData": "Selecione uma bolha, seguida por um ponto de dados para exibir o gráfico de importância local", + "localImportanceFetchError": "Erro ao buscar os dados de importância local. Detalhes do erro: {0} Verifique os dados usados.", + "BubbleChartFetchError": "Ocorreu um erro ao buscar os dados. Detalhes do erro: {0}Verifique os dados usados.", "noData": "Nenhum dado", "noFeatures": "Nenhum recursos disponíveis", "panelDescription": "Procure contrafactuais e crie seus próprios. Pesquise recursos para ver os valores sugeridos de um conjunto diversificado de exemplos contrafactuais. Defina valores de recursos contrafactuais sugeridos clicando em \" Definir Valor\" texto em cada nome contrafactual. Nomeie seu contrafactual e salve-o.", @@ -223,13 +223,13 @@ "subText": "Saiba mais sobre o coorte selecionado. Edite seu nome de coorte. Excluir este coorte." }, "FeatureList": { - "featureList": "Feature List", + "featureList": "Lista de Recursos", "apply": "Aplicar", "features": "Recursos", "importances": "Importâncias", "treeMapDescription": "Para treinar novamente o mapa da árvore, selecione e salve os recursos abaixo. As importâncias do recurso foram calculadas utilizando informações mútuas com o erro nos rótulos verdadeiros. Utilize como uma diretriz para o treinamento do mapa de árvore.", "staticTreeMapDescription": "Exiba os recursos que foram usados para treinar o mapa de árvore. As importâncias do recurso foram calculadas usando informações mútuas com o erro nos rótulos verdadeiros.", - "searchResultMessage": "Results displayed out of {resultLength} for {searchValue}" + "searchResultMessage": "Resultados exibidos de {resultLength} para {searchValue}" }, "TreeViewParameters": { "maximumDepth": "Profundidade máxima", @@ -295,7 +295,7 @@ "disabledWarning": "O mapa de calor de erro está desabilitado, a menos que o coorte global seja alternado para representar \"Todos os dados\" devido ao mapa de calor que está sendo gerado para o conjunto de dados completo. Volte para o conjunto de dados completo para exibir o mapa de calor de erro." }, "MatrixSummary": { - "heatMapInfoTitle": "Additional information on heat map", + "heatMapInfoTitle": "Mais informações sobre mapa de calor.", "heatMapDescription": "Com o mapa de calor, você pode se concentrar em filtros de recursos interseccionais específicos e calcular taxas de erro desagregadas. Comece com dois recursos de conjunto de dados para comparar.", "heatMapStaticDescription": "Com o mapa de calor, você pode se concentrar em filtros de recursos intersecionais específicos e taxas de erro de computação desagregadas. Até dois recursos devem ser selecionados para criar um mapa de calor por meio do SDK antes de exibir o painel." }, @@ -311,108 +311,108 @@ }, "Metrics": { "AccuracyScore": { - "Name": "Accuracy score", - "Info": "The accuracy score represents the ratio of correct to total instances in the data.", - "Short": "Accuracy", - "Title": "Additional information on accuracy score" + "Name": "Pontuação de precisão", + "Info": "A pontuação de precisão representa a proporção de instâncias corretas para o total nos dados.", + "Short": "Precisão", + "Title": "Informações adicionais sobre a pontuação de precisão" }, "ErrorRate": { - "Name": "Error rate", - "Info": "The error rate represents the percentage of instances in the node for which the system has failed.", - "Short": "Error rate", - "Title": "Additional information on error rate" + "Name": "Taxa de erro", + "Info": "A taxa de erro representa o percentual de instâncias no nó para as quais o sistema falhou.", + "Short": "Taxa de erro", + "Title": "Informações adicionais sobre a taxa de erro" }, "F1Score": { - "Name": "F1 score", - "Info": "The F1 score is the harmonic mean of the precision and recall metrics.", - "Short": "F1 score", - "Title": "Additional information on F1 score" + "Name": "Pontuação F1", + "Info": "A pontuação F1 é a média da precisão e das métricas de recall.", + "Short": "Pontuação F1", + "Title": "Informações adicionais sobre a pontuação F1" }, "MeanAbsoluteError": { - "Name": "Mean absolute error", - "Info": "The mean absolute error is the average of the sum of the errors.", - "Short": "Mean abs. error", - "Title": "Additional information on mean absolute error" + "Name": "Erro médio absoluto", + "Info": "O erro médio absoluto é a média da soma dos erros.", + "Short": "Média de erro abs.", + "Title": "Informações adicionais sobre erro médio absoluto" }, "MeanSquaredError": { - "Name": "Mean squared error", - "Info": "The mean squared error is the average of the squares of the errors.", - "Short": "Mean sq. error", - "Title": "Additional information on mean squared error" + "Name": "Erro médio ao quadrado", + "Info": "O erro quadrático médio é a média dos quadrados dos erros.", + "Short": "Erro de média sq.", + "Title": "Informações adicionais sobre o erro médio quadrado" }, "Precision": { - "Name": "Precision score", - "Info": "The precision is the ratio of true positives over all predicted positives.", - "Short": "Precision", - "Title": "Additional information on precision" + "Name": "Pontuação de precisão", + "Info": "A precisão é a taxa de verdadeiros positivos em relação a todos os positivos previstos.", + "Short": "Precisão", + "Title": "Informações adicionais sobre precisão" }, "Recall": { - "Name": "Recall score", - "Info": "The recall is the ratio of true positives over all actual positives.", + "Name": "Pontuação de recall", + "Info": "O recall é a proporção de verdadeiros positivos em relação a todos os positivos reais.", "Short": "Recall", - "Title": "Additional information on recall" + "Title": "Informações adicionais sobre as células" }, "MacroPrecision": { - "Name": "Macro averaged precision score", - "Info": "The macro averaged precision is the ratio of true positives over all predicted positives computed independently per class and averaged.", - "Short": "Macro precision", - "Title": "Additional information on macro averaged precision" + "Name": "Pontuação de precisão média macro", + "Info": "A precisão média macro é a proporção de verdadeiros positivos em relação a todos os positivos previstos calculados independentemente por classe e média.", + "Short": "Precisão macro", + "Title": "Informações adicionais sobre a precisão média macro" }, "MicroPrecision": { - "Name": "Micro averaged precision score", - "Info": "The micro averaged precision is the ratio of true positives over all predicted positives aggregated for all classes.", - "Short": "Micro precision", - "Title": "Additional information on micro averaged precision" + "Name": "Pontuação de precisão média micro", + "Info": "A precisão média micro é a proporção de verdadeiros positivos sobre todos os positivos previstos agregados para todas as classes.", + "Short": "Micro precisão", + "Title": "Informações adicionais sobre precisão média micro" }, "MacroRecall": { - "Name": "Macro averaged recall score", - "Info": "The macro averaged recall is the ratio of true positives over all actual positives computed independently per class and averaged.", - "Short": "Macro recall", - "Title": "Additional information on macro averaged recall" + "Name": "Pontuação média de recall macro", + "Info": "A média de recall macro é a taxa de verdadeiros positivos em relação a todos os positivos reais calculados independentemente por classe e média.", + "Short": "Recall macro", + "Title": "Informações adicionais sobre a média de recall macro" }, "MicroRecall": { - "Name": "Micro averaged recall score", - "Info": "The micro averaged recall is the ratio of true positives over all actual positives aggregated for all classes.", - "Short": "Micro recall", - "Title": "Additional information on micro averaged recall" + "Name": "Pontuação média de recall micro", + "Info": "A média de recall micro é a proporção de verdadeiros positivos em relação a todos os positivos reais agregados para todas as classes.", + "Short": "Recall micro", + "Title": "Informações adicionais sobre o recall médio micro" }, "MacroF1Score": { - "Name": "Macro averaged F1 score", - "Info": "The macro averaged F1 score is the harmonic mean of the macro averaged precision and recall metrics.", - "Short": "Macro F1 score", - "Title": "Additional information on macro averaged F1 score" + "Name": "Pontuação média de F1 da macro", + "Info": "A pontuação F1 média macro é a média harmônica da precisão média macro e das métricas de recall.", + "Short": "Pontuação de macro F1", + "Title": "Informações adicionais sobre a pontuação média de F1 da macro" }, "MicroF1Score": { - "Name": "Micro averaged F1 score", - "Info": "The micro averaged F1 score is the harmonic mean of the micro averaged precision and recall metrics.", - "Short": "Micro F1 score", - "Title": "Additional information on micro averaged F1 score" + "Name": "Pontuação de F1 média micro", + "Info": "A pontuação F1 de micro média é a média harmônica das métricas de precisão e recall de micro média.", + "Short": "Pontuação de Micro F1", + "Title": "Informações adicionais sobre a pontuação de F1 média micro" }, "MeanAveragePrecision": { - "Name": "Mean average precision score", - "Info": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall.", - "Short": "Mean avg precision", - "Title": "Additional information on mean average precision score" + "Name": "Pontuação média de precisão", + "Info": "A precisão média média para modelos de detecção de objetos é a média de AP (precisão média) em todas as classes. Isso avalia a robustez do modelo de detecção de objetos e encapsula a compensação entre precisão e recall.", + "Short": "Precisão média", + "Title": "Informações adicionais sobre pontuação de precisão média" }, "AveragePrecision": { - "Name": "Average precision score", - "Info": "Average precision for object detection models is calculated for a selected class.", - "Short": "Avg precision", - "Title": "Additional information on average precision score" + "Name": "Pontuação de precisão média", + "Info": "A precisão média dos modelos de detecção de objetos é calculada para uma classe selecionada.", + "Short": "Precisão média", + "Title": "Informações adicionais sobre pontuação de precisão média" }, "AverageRecall": { - "Name": "Average recall score", - "Info": "Average recall for object detection models is calculated for a selected class.", - "Short": "Avg recall", - "Title": "Additional information on average recall score" + "Name": "Pontuação média de rechamada", + "Info": "O recall médio para modelos de detecção de objetos é calculado para uma classe selecionada.", + "Short": "Recordação média", + "Title": "Informações adicionais sobre a pontuação média de recordação" }, "metricName": "Nome da métrica", "metricValue": "Valor da métrica" }, "MetricSelector": { "selectorLabel": "Selecionar métrica", - "feature1SelectorLabel": "Rows: Feature 1", - "feature2SelectorLabel": "Columns: Feature 2" + "feature1SelectorLabel": "Linhas: Recurso 1", + "feature2SelectorLabel": "Colunas: Recurso 2" }, "Navigation": { "cohortSaved": "O novo coorte foi salvo! Consulte a lista coorte nas configurações de Coorte.", @@ -433,9 +433,9 @@ "defaultLabelCopy": "Cópia de todos os dados" }, "TreeView": { - "ariaLabel": "Interactive chart", - "disabledArialLabel": "Disabled interactive chart", - "treeMapInfoTitle": "Additional information on tree map", + "ariaLabel": "Gráfico interativo", + "disabledArialLabel": "Gráfico interativo desabilitado", + "treeMapInfoTitle": "Informações adicionais sobre a pontuação F1", "treeDescription": "A visualização em árvore usa as informações mútuas entre cada recurso e o erro para separar melhor as instâncias de erro das instâncias de sucesso hierarquicamente nos dados. Isso simplifica o processo de descoberta e destaque de padrões de falhas comuns. Para encontrar padrões de falha importantes, procure por nós com uma cor vermelha mais forte (ou seja, alta taxa de erro) e uma linha de preenchimento mais alta (ou seja, alta cobertura de erro). Para editar a lista de recursos que estão sendo usados na árvore, clique em \"Lista de recursos\". Use o menu suspenso \"selecionar métrica\" para saber mais sobre o desempenho dos nós de erro e sucesso. Observe que essa seleção de métrica não afetará a maneira como sua árvore de erros é gerada.", "treeStaticDescription": "A visualização em árvore usa as informações mútuas entre cada recurso e o erro para separar melhor as instâncias de erro das instâncias de sucesso hierarquicamente nos dados. Isso simplifica o processo de descoberta e destaque de padrões de falhas comuns. Para encontrar padrões de falha importantes, procure por nós com uma cor vermelha mais forte (ou seja, alta taxa de erro) e uma linha de preenchimento mais alta (ou seja, alta cobertura de erro). Para visualizar a lista de recursos usados na criação dessa árvore de erros, clique em \"Lista de recursos\". Use o menu suspenso \"selecionar métrica\" para saber mais sobre o desempenho dos nós de erro e sucesso. Observe que essa seleção de métrica não afetará a maneira como sua árvore de erros é gerada.", "disabledWarning": "O mapa de árvore de erro está desabilitado, a menos que o coorte global seja alternado para representar \"Todos os dados\" devido ao mapa de árvore que está sendo gerado para o conjunto de dados completo. Volte para o conjunto de dados completo para exibir o mapa de árvore de erro." @@ -770,7 +770,7 @@ "countHelperText": "Um histograma do número de pontos", "ditherLabel": "Deveria pontilhar", "groupByCohort": "Agrupar por coorte", - "logarithmicScaling": "Enable logarithmic scaling", + "logarithmicScaling": "Habilitar a escala logarítmica", "numOfBins": "Número de compartimentos", "selectClass": "Selecionar a classe", "selectFeature": "Selecionar o recurso", @@ -794,7 +794,7 @@ "importancePrefix": "Importância", "numberOfDatapoints": "Número de pontos de dados", "rowIndex": "Índice de linha", - "absoluteIndex": "Absolute index", + "absoluteIndex": "Índice absoluto", "xValue": "Valor X", "yValue": "Valor Y" }, @@ -822,12 +822,12 @@ }, "CohortEditor": { "columns": { - "index": "Index", - "dataset": "Dataset", - "predictedY": "Predicted Y", - "trueY": "True Y", - "classificationOutcome": "Classification outcome", - "regressionError": "Error" + "index": "Índice", + "dataset": "Conjunto de Dados", + "predictedY": "Y Previsto", + "trueY": "Y Verdadeiro", + "classificationOutcome": "Resultado da classificação", + "regressionError": "Erro" }, "TreatAsCategorical": "Tratar como categórico", "addFilter": "Adicionar um filtro", @@ -852,8 +852,8 @@ "save": "Salvar", "saveAndSwitch": "Salvar e alternar", "selectFilter": "Selecionar um filtro", - "noFiltersApplied": "No filters applied", - "filterAdded": "Filter added" + "noFiltersApplied": "Nenhum filtro aplicado", + "filterAdded": "Filtro adicionado" }, "Columns": { "classificationOutcome": "Resultado da classificação", @@ -863,8 +863,8 @@ "falsePositive": "Falso positivo", "none": "Contagem", "predictedProbabilities": "Probabilidades de previsão", - "predictedLabels": "Predicted labels", - "trueLabels": "True labels", + "predictedLabels": "Rótulos previsto", + "trueLabels": "Rótulos verdadeiros", "regressionError": "Erro de regressão", "trueNegative": "Verdadeiro negativo", "truePositive": "Verdadeiro positivo", @@ -885,7 +885,7 @@ "aggregatePlots": "Gráficos agregados", "chartType": "Tipo de gráfico", "colorValue": "Valor da cor", - "infoTitle": "Additional information on data analysis chart view", + "infoTitle": "Informações adicionais sobre a exibição do gráfico de análise de dados", "helperText": "Crie coortes de conjunto de dados para analisar estatísticas de conjunto de dados ao longo de filtros, como resultado previsto, recursos de conjunto de dados e grupos de erros. Saiba mais sobre a apresentação em excesso em seu conjunto de dados.", "individualDatapoints": "Pontos de dados individuais", "missingParameters": "Esta guia requer que um conjunto de dados de avaliação seja fornecido.", @@ -906,6 +906,8 @@ "index": "Índice", "output": "Saída", "predictedY": "Y Previsto", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Probabilidade : {0}", "trueY": "Y Verdadeiro", "xValue": "Valor X:", @@ -974,10 +976,10 @@ "dependencePlotHelperText": "Esse gráfico de dependência mostra a relação dos valores de um recurso com seus valores de importância do recurso correspondente.", "dependencePlotTitle": "Gráficos de dependência", "helperText": "Explore os principais recursos importantes que impactam as previsões gerais do modelo (também conhecida como explicação global). Use o controle deslizante para mostrar as importâncias do recurso decrescente. As importâncias do recurso de todas as coortes são mostradas lado a lado e podem ser desativadas selecionando a coorte na legenda. Clique em qualquer um dos recursos no gráfico para ver um gráfico de densidade abaixo de como os valores do recurso selecionado afetam a previsão.", - "infoTitle": "Additional information on aggregate feature importance", + "infoTitle": "Informações adicionais sobre a importância do recurso agregado", "legendHelpText": "Ative e desative coortes no gráfico clicando nos itens da legenda.", "missingParameters": "Esta guia requer que seja fornecido o parâmetro de importância do recurso local.", - "sortByCohort": "Sort by cohort", + "sortByCohort": "Classificar por coorte", "sortBy": "Classificar por ponto de dados", "topAtoB": "Principais recursos {0} por importância", "viewDependencePlotFor": "Exibir o gráfico de dependência para:", @@ -1020,15 +1022,15 @@ }, "Statistics": { "accuracy": "Precisão: {0}", - "bleuScore": "Bleu score: {0}", - "bertScore": "Bert score: {0}", - "exactMatchRatio": "Exact match ratio: {0}", - "rougeScore": "Rouge Score: {0}", + "bleuScore": "Pontuação Bleu: {0}", + "bertScore": "Pontuação Bert: {0}", + "exactMatchRatio": "Taxa de correspondência exata: {0}", + "rougeScore": "Pontuação Rouge: {0}", "fnr": "Taxa de falsos negativos: {0}", "fpr": "Taxa de falsos positivos: {0}", - "hammingScore": "Hamming score: {0}", + "hammingScore": "Pontuação Hamming: {0}", "meanPrediction": "Previsão média {0}", - "meteorScore": "Meteor Score: {0}", + "meteorScore": "Pontuação Meteoro: {0}", "mse": "Erro quadrado médio: {0}", "precision": "Precisão: {0}", "rSquared": "R²: {0}", @@ -1036,10 +1038,10 @@ "selectionRate": "Taxa de seleção: {0}", "mae": "Erro absoluto de média: {0}", "f1Score": "Pontuação F1: {0}", - "samples": "Sample size: {0}", - "meanAveragePrecision": "Mean average precision: {0}", - "averagePrecision": "Average precision: {0}", - "averageRecall": "Average recall: {0}" + "samples": "Tamanho do exemplo: {0}", + "meanAveragePrecision": "Precisão média: {0}", + "averagePrecision": "Precisão média: {0}", + "averageRecall": "Recall médio: {0}" }, "ValidationErrors": { "addFilters": "Adicionar filtros", @@ -1147,30 +1149,30 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Painel de Interpretabilidade", - "importantWords": "Show most important words", + "importantWords": "Exibir as palavras mais importantes", "topFeatureList": "Análise da lista de recursos principais", "allButton": "TODOS OS RECURSOS", "negButton": "RECURSOS NEGATIVOS", "posButton": "RECURSOS POSITIVOS", - "legendText": "Positive scalar feature importances represent the extent that the words were important towards the classification of your selected label, and negative scalar feature importances represent words that encouraged your model away from your selected label.", - "legendTextForQA": "The left text box and the bar chart display the predictions of the model. The right text box shows the feature importance associated with a selected token. Positive feature importances represent the extent that the words were important towards marking the selected token as the starting/ending position of the answer.", + "legendText": "As importâncias positivas do recurso escalar representam a importância de uma palavra para a classificação do rótulo selecionado, e as importâncias negativas do recurso escalar representam palavras que incentivaram o modelo a sair do rótulo selecionado.", + "legendTextForQA": "A caixa de texto à esquerda e o gráfico de barras exibem as previsões do modelo. A caixa de texto à direita mostra a importância do recurso associado a um token selecionado. As importâncias de características positivas representam a extensão em que as palavras foram importantes para marcar o token selecionado como a posição inicial/final da resposta.", "label": "Rótulo", "colon": ": ", - "startingPosition": "STARTING POSITION", - "endingPosition": "ENDING POSITION", - "predictedAnswer": "Predicted answer: ", - "trueAnswer": "True answer: ", - "inputs": "Inputs", - "outputs": "Outputs", - "sliderAriaLabel": "Slider for most important words" + "startingPosition": "POSICÃO INICIAL", + "endingPosition": "POSIÇÃO FINAL", + "predictedAnswer": "Resposta prevista: ", + "trueAnswer": "Resposta verdadeira: ", + "inputs": "Entradas", + "outputs": "Saídas", + "sliderAriaLabel": "Controle deslizante das palavras mais importantes" }, "Legend": { "featureLegend": "LEGENDA DO RECURSO DE TEXTO", "posFeatureImportance": "IMPORTÂNCIA POSITIVA DO RECURSO", "negFeatureImportance": "IMPORTÂNCIA NEGATIVA DO RECURSO", - "cls": "CLS: start of the sentence", - "sep": "SEP: end of the sentence", - "selectedWord": "Selected word: " + "cls": "CLS: início da frase", + "sep": "SEP: fim da frase", + "selectedWord": "Palavra selecionada: " }, "BarChart": { "featureImportance": "IMPORTÂNCIA DO RECURSO" @@ -1178,59 +1180,59 @@ }, "InterpretVision": { "Cohort": { - "close": "Close", - "errorCohortName": "Please choose a unique cohort name.", - "errorNumSelected": "Please select at least one (1) item.", - "itemsSelectedSingular": "item selected", - "itemsSelectedPlural": "items selected", - "save": "Save cohort", - "saveAndClose": "Save and close", - "saveAndSwitch": "Save and switch", - "textField": "New cohort name", - "title": "Save new cohort" + "close": "Fechar", + "errorCohortName": "Escolha um nome de coorte exclusivo.", + "errorNumSelected": "Selecione pelo menos um (1) item.", + "itemsSelectedSingular": "item selecionado", + "itemsSelectedPlural": "itens selecionados", + "save": "Salvar coorte", + "saveAndClose": "Salvar e fechar", + "saveAndSwitch": "Salvar e alternar", + "textField": "Novo nome de coorte", + "title": "Salvar novo coorte" }, "Dashboard": { "allData": "Todos os Dados", - "columnOne": "Image", + "columnOne": "Imagem", "columnTwo": "Índice", "columnThree": "Y Verdadeiro", "columnFour": "Y Previsto", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Correto", + "columnFourOD": "Incorreto", "columnFive": "Outros metadados", - "chooseObject": "Choose a detected object", - "examples": "examples", + "chooseObject": "Escolher um objeto detectado", + "examples": "exemplos", "filter": "Filtrar", - "indexLabel": "Image ", - "labelTypeDropdown": "Select label type", - "labelVisibilityDropdown": "Select labels to display", - "legendFailure": "failure", - "legendSuccess": "success", - "loading": "Computing explanation for index", - "multiselect": "Multiselect", - "notdefined": "object scenario not defined", - "objectSelect": "Object Selection", + "indexLabel": "Imagem ", + "labelTypeDropdown": "Selecionar tipo de rótulo", + "labelVisibilityDropdown": "Selecionar rótulos a serem exibidos", + "legendFailure": "falha", + "legendSuccess": "bem-sucedido", + "loading": "Explicação de computação para índice", + "multiselect": "Multisseleção", + "notdefined": "cenário de objeto não definido", + "objectSelect": "Seleção de objeto", "pageSize": "Tamanho da página: ", - "panelTitle": "Selected instance", - "panelExplanation": "Explanation", - "panelInformation": "Information", - "predictedLabel": "Predicted label", - "predictedY": "Predicted: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", - "prefix": "Object: ", - "rows": "Rows: ", + "panelTitle": "Instância selecionada", + "panelExplanation": "Explicação", + "panelInformation": "Informação", + "predictedLabel": "Rótulo previsto", + "predictedY": "Previsto:", + "correctDetections": "Detecções corretas: ", + "incorrectDetections": "Detecções incorretas: ", + "prefix": "Objeto: ", + "rows": "Linhas: ", "search": "Pesquisar", - "selectAll": "Select all", + "selectAll": "Selecionar tudo", "settings": "Configurações", - "showAll": "Show all", + "showAll": "Mostrar tudo", "tabOptionFirst": "Exibição do Explorador de Imagens", "tabOptionSecond": "Modo de exibição de tabela", - "tabOptionThird": "Class view", + "tabOptionThird": "Modo de exibição de classe", "thumbnailSize": "Tamanho da miniatura", "titleBarError": "Instâncias de erro", "titleBarSuccess": "Instâncias de sucesso", - "trueY": "Ground truth: " + "trueY": "Verdade básica:" } }, "ModelAssessment": { @@ -1239,15 +1241,15 @@ "CalloutContent": "A adição de alguns componentes (modo de exibição de árvore de erro, mapa de calor de erro) permitirão filtrar os dados da coorte global que você vê nos componentes abaixo.", "CalloutTitle": "Adicionar um componente", "TabAddedMessage": { - "DataAnalysis": "Data analysis component added", - "FeatureImportances": "Feature importances component added", - "ErrorAnalysis": "Error analysis component added", - "Fairness": "Fairness component added", - "ModelOverview": "Model overview component added", - "CausalAnalysis": "Causal analysis component added", - "Counterfactuals": "Counterfactuals component added", - "Vision": "Vision data explorer component added", - "Forecasting": "Forecasting what-if component added" + "DataAnalysis": "Componente de análise de dados adicionado", + "FeatureImportances": "Componente de importâncias do recurso adicionado", + "ErrorAnalysis": "Componente de análise de erro adicionado", + "Fairness": "Componente de imparcialidade adicionado", + "ModelOverview": "Componente de visão geral do modelo adicionado", + "CausalAnalysis": "Componente de análise causal adicionado", + "Counterfactuals": "Componente de contrafactuais adicionado", + "Vision": "Componente do Gerenciador de Dados de Visão adicionado", + "Forecasting": "Componente what-if de previsão adicionado" } }, "CausalAnalysis": { @@ -1275,7 +1277,7 @@ }, "CohortInformation": { "ShiftCohort": "Alternar coorte", - "SwitchTimeSeries": "Switch time series", + "SwitchTimeSeries": "Alternar série temporal", "NewCohort": "Novo coorte", "DataPoints": "Número de pontos de dados", "DefaultCohort": " (padrão)", @@ -1287,7 +1289,7 @@ "CohortSettingsTitle": "Configurações de coorte" }, "ComponentNames": { - "ChartView": "Chart view", + "ChartView": "Exibição de gráfico", "CausalAnalysis": "Análise de causa", "Counterfactuals": "Contrafatuais", "DataAnalysis": "Análise de dados", @@ -1296,10 +1298,10 @@ "ErrorAnalysis": "Análise de erro", "Fairness": "Imparcialidade", "FeatureImportances": "Importâncias do recurso", - "Forecasting": "Forecasting", + "Forecasting": "Previsão", "ModelOverview": "Visão geral do modelo", - "TableView": "Table view", - "VisionTab": "Vision data explorer" + "TableView": "Modo de exibição de tabela", + "VisionTab": "Gerenciador de dados de visão" }, "DashboardSettings": { "Content": "Esta lista mostra o layout do painel. Você pode filtrar os dados usando o componente de análise de erro para ser exibido nos componentes abaixo.", @@ -1458,16 +1460,16 @@ "GlobalExplanation": "Importância agregada do recurso", "IncorrectPredictions": "Previsões incorretas", "InfoTitle": "Additional information on feature importance values", - "IndividualFeatureTabular": "Select a datapoint by clicking on a datapoint (up to 5 datapoints) in the table to view their local feature importance values (local explanation) and individual conditional expectation (ICE) plots.", + "IndividualFeatureTabular": "Selecione um ponto de dados clicando em um ponto de dados (até 5 pontos de dados) na tabela para exibir os valores de importância do recurso local (explicação local) e o gráfico de expectativa condicional individual (ICE) abaixo.", "IndividualFeatureText": "Select a datapoint by clicking on a datapoint in the table to view the local feature importance values (local explanation) from SHAP's text explainer.", "LocalExplanation": "Importância de recursos individuais", "SelectionCounter": "{0}/{1} pontos de dados selecionados", "SelectionLimit": "Até 5 pontos de dados podem ser selecionados no momento.", - "RowCheckboxAriaLabel": "Row checkbox", - "SelectionColumnAriaLabel": "Toggle selection" + "RowCheckboxAriaLabel": "Caixa de seleção de linha", + "SelectionColumnAriaLabel": "Ativar/Desativar a seleção" }, "IndividualFeatureImportanceView": { - "SmallInstanceSelection": "Instance selection" + "SmallInstanceSelection": "Seleção de instância" }, "MainMenu": { "DashboardSettings": "Configuração do painel", @@ -1483,44 +1485,44 @@ "ModelOverview": { "metrics": { "accuracy": { - "name": "Accuracy score", + "name": "Pontuação de precisão", "description": "A fração de pontos de dados classificados corretamente." }, "exactMatchRatio": { - "name": "Exact match ratio", - "description": "The ratio of instances classified correctly for every label." + "name": "Taxa de correspondência exata", + "description": "A taxa de instâncias classificadas corretamente para cada rótulo." }, "meteorScore": { - "name": "Meteor Score", - "description": "METEOR Score is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision in question answering task." + "name": "Pontuação Meteoro", + "description": "A Pontuação METEORO é calculada com base na média de precisão e recall, com recall ponderado mais do que precisão na tarefa de resposta de perguntas." }, "bleuScore": { - "name": "Bleu Score", - "description": "Bleu Score measures the ratio of words (and/or n-grams) in the machine generated text that appeared in the reference text in question answering task." + "name": "Pontuação Bleu", + "description": "O Bleu Score mede a proporção de palavras (e/ou n-gramas) no texto gerado pelo computador que apareceu no texto de referência na tarefa de resposta de perguntas." }, "bertScore": { - "name": "Bert Score", - "description": "BERTScore focuses on computing semantic similarity between tokens of reference and machine generated text in question answering task." + "name": "Pontuação Bert", + "description": "O BERTScore concentra-se na similaridade semântica de computação entre tokens de referência e texto gerado pelo computador na tarefa de resposta de perguntas." }, "rougeScore": { - "name": "Rouge Score", - "description": "Rouge Score measures the ratio of words (and/or n-grams) in the reference text that appeared in the machine generated text in question answering task." + "name": "Pontuação Rouge", + "description": "A Pontuação Rouge mede a proporção de palavras (e/ou n-somente) no texto de referência que apareceu no texto gerado pelo computador na tarefa de resposta de perguntas." }, "hammingScore": { - "name": "Hamming score", - "description": "The average ratio of labels classified correctly among those classified as 1 in multilabel task." + "name": "Pontuação Hamming", + "description": "A taxa média de rótulos classificados corretamente entre aqueles classificados como 1 na tarefa multi-rótulo." }, "f1Score": { "name": "Pontuação F1", "description": "A pontuação F1 é a média harmônica de precisão e recall." }, "f1ScoreMacro": { - "name": "Macro F1 score", - "description": "Macro F1 score is the harmonic mean of precision and recall for each class, with each class weighted equally." + "name": "Pontuação de macro F1", + "description": "A pontuação de macro F1 é a média de precisão e recall de cada classe, com cada classe ponderada igualmente." }, "f1ScoreMicro": { - "name": "Micro F1 score", - "description": "Micro F1 score is the harmonic mean of precision and recall for each class, with each class weighted according to how many instances it contains." + "name": "Pontuação de Micro F1", + "description": "A pontuação micro F1 é a média de precisão e recall de cada classe, com cada classe ponderada de acordo com quantas instâncias ela contém." }, "meanAbsoluteError": { "name": "Erro médio absoluto", @@ -1535,24 +1537,24 @@ "description": "A fração de pontos de dados classificados corretamente entre aqueles classificados como 1." }, "precisionMacro": { - "name": "Macro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted equally." + "name": "Pontuação de Precisão de Macro", + "description": "A fração de pontos de dados classificados corretamente entre aqueles classificados como 1 para cada classe com cada classe ponderada igualmente." }, "precisionMicro": { - "name": "Micro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted according to how many instances it contains." + "name": "Pontuação de Micro precisão", + "description": "A fração de pontos de dados classificados corretamente entre aqueles classificados como 1 para cada classe com cada classe ponderada de acordo com quantas instâncias ela contém." }, "recall": { "name": "Pontuação de recall", "description": "A fração de pontos de dados classificados corretamente entre aqueles cujo rótulo verdadeiro é 1. Nomes alternativos: taxa de verdadeiro positivo, confidencialidade." }, "recallMacro": { - "name": "Macro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted equally." + "name": "Pontuação de Recall de Macro", + "description": "A fração de pontos de dados classificados corretamente entre aqueles cujo rótulo verdadeiro é 1 para cada classe com cada classe ponderada igualmente." }, "recallMicro": { - "name": "Micro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted according to how many instances it contains." + "name": "Pontuação de Micro Recall", + "description": "A fração de pontos de dados classificados corretamente entre aqueles cujo rótulo verdadeiro é 1 para cada classe com cada classe ponderada de acordo com quantas instâncias ela contém." }, "falsePositiveRate": { "name": "Classificação de falso positivo", @@ -1571,32 +1573,32 @@ "description": "A média de todas as previsões." }, "meanAveragePrecision": { - "name": "Mean Average Precision score", - "description": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall." + "name": "Micro de pontuação de precisão média", + "description": "A precisão média média para modelos de detecção de objetos é a média de AP (precisão média) em todas as classes. Isso avalia a robustez do modelo de detecção de objetos e encapsula a compensação entre precisão e recall." }, "averagePrecision": { - "name": "Average Precision score", - "description": "Average precision for object detection models is calculated for a selected class." + "name": "Pontuação de precisão média", + "description": "A precisão média dos modelos de detecção de objetos é calculada para uma classe selecionada." }, "averageRecall": { - "name": "Average Recall score", - "description": "Average recall for object detection models is calculated for a selected class." + "name": "Pontuação média de recall", + "description": "O recall médio para modelos de detecção de objetos é calculado para uma classe selecionada." }, "fairnessMetricDifference": "Diferença", "fairnessMetricRatio": "Proporção" }, "metricsDropdown": "Métrica(s)", - "metricsTypeDropdown": "Aggregate method", + "metricsTypeDropdown": "Método de agregação", "metricTypes": { "macro": "Macro", "micro": "Micro" }, - "classSelectionDropdown": "Select class(es)", + "classSelectionDropdown": "Selecionar as classes", "iouThresholdDropdown": { - "name": "IoU Threshold", - "description": "Intersection over Union quantifies the degree of overlap between the prediction and ground truth bounding box of a detected object in an image. For example, setting an IoU threshold of 70% means that a prediction with greater than 70% overlap with ground truth is True, thus influencing the definition of prediction correctness and calculation of other performance metrics.", + "name": "Limite de IoU:", + "description": "A interseção sobre a União quantifica o grau de sobreposição entre a previsão e a caixa delimitadora da verdade base de um objeto detectado em uma imagem. Por exemplo, definir um limite de IoU de 70% significa que uma previsão com mais de 70% sobreposição com a verdade base é True, influenciando assim a definição de correção de previsão e o cálculo de outras métricas de desempenho.", "iconId": "iouThresholdIconId", - "title": "Learn about the IoU threshold" + "title": "Saiba mais sobre o limite de IoU" }, "notAvailable": "N/D", "countColumnHeader": "Tamanho da amostra", @@ -1608,14 +1610,14 @@ "featuresDropdown": "Recursos", "metricChartDropdownSelectionHeader": "Métrica", "probabilityForClassSelectionHeader": "Probabilidade de classe", - "targetSelectionHeader": "Target", + "targetSelectionHeader": "Destino", "metricSelectionDropdownPlaceholder": "Selecione as métricas para comparar seus coortes.", - "classSelectionDropdownPlaceholder": "Select class name for class-based analysis.", + "classSelectionDropdownPlaceholder": "Selecione o nome da classe para análise baseada em classe.", "featureSelectionDropdownPlaceholder": "Selecione os recursos a serem usado para uma análise baseada em recurso.", "probabilityDistributionPivotItem": "Distribuição de probabilidade", - "regressionDistributionPivotItem": "Target distribution", + "regressionDistributionPivotItem": "Distribuição de destino", "metricsVisualizationsPivotItem": "Visualizações de métricas", - "confusionMatrixPivotItem": "Confusion matrix", + "confusionMatrixPivotItem": "Matriz de confusão", "disaggregatedAnalysisFeatureSelectionPlaceholder": "Selecione os recursos para gerar a análise baseada em recursos.", "tableCountTooltip": "Coorte {0} contém instâncias {1}.", "tableMetricTooltip": "O {0} no coorte {1} é {2}", @@ -1626,36 +1628,36 @@ "metricSelectionButton": "Escolher métrica", "cohortSelectionButton": "Escolher coortes", "probabilityLabelSelectionButton": "Escolher rótulo", - "regressionTargetSelectionButton": "Choose target", + "regressionTargetSelectionButton": "Escolher destino", "selectAllCohortsOption": "Selecionar tudo", "other": "Outro", "BoxPlot": { "outlierProbability": "probabilidade", "outlierLabel": "Exceções", "boxPlotSeriesLabel": "Plotagem de Caixa", - "lowerWhisker": "Lower whisker", - "upperWhisker": "Upper whisker", - "median": "Median", - "lowerQuartile": "Lower quartile", - "upperQuartile": "Upper quartile" + "lowerWhisker": "Estreita inferior", + "upperWhisker": "Caixa estreita superior", + "median": "Mediano", + "lowerQuartile": "Quartil inferior", + "upperQuartile": "Quartil superior" }, "chartConfigApply": "Aplicar", "chartConfigCancel": "Cancelar", "chartConfigDatasetCohortSelectionPlaceholder": "Selecionar coortes de conjunto de dados", "chartConfigFeatureBasedCohortSelectionPlaceholder": "Selecionar coortes baseados em recurso", "confusionMatrix": { - "confusionMatrixCohortSelectionLabel": "Select dataset cohort", - "confusionMatrixClassSelectionLabel": "Select classes", - "confusionMatrixClassMinSelectionError": "Select at least {0} classes to visualize the confusion matrix.", - "confusionMatrixClassMaxSelectionError": "Select at most {0} classes to visualize the confusion matrix.", - "confusionMatrixClassSelectionDefaultPlaceholder": "Choose classes", - "confusionMatrixHeatmapTooltip": "{0} datapoints should be {1}, predicted to be {2}", - "confusionMatrixYAxisLabel": "True Class", - "confusionMatrixXAxisLabel": "Predicted Class", - "class": "Class" + "confusionMatrixCohortSelectionLabel": "Selecionar coortes de conjunto de dados", + "confusionMatrixClassSelectionLabel": "Selecionar classes", + "confusionMatrixClassMinSelectionError": "Selecione pelo menos {0} classes para visualizar a matriz de confusão.", + "confusionMatrixClassMaxSelectionError": "Selecione no máximo {0} classes para visualizar a matriz de confusão.", + "confusionMatrixClassSelectionDefaultPlaceholder": "Escolher classes", + "confusionMatrixHeatmapTooltip": "{0} pontos de dados devem ser {1}, previsto para ser {2}", + "confusionMatrixYAxisLabel": "Classe verdadeira", + "confusionMatrixXAxisLabel": "Classe Prevista", + "class": "Classe" }, "nA": "N/D", - "disaggregatedAnalysisBaseCohortDisclaimer": "The cohorts in the following feature-based analysis are based on the global cohort, {0}.", + "disaggregatedAnalysisBaseCohortDisclaimer": "Os coortes na análise baseada em recurso a seguir são baseados no coorte global, {0}.", "disaggregatedAnalysisBaseCohortWarning": "Ao contrário do coorte {0}, {1} inclui filtros. Como consequência, ele captura apenas um subconjunto de todo o conjunto de dados e insights podem não generalizar para o conjunto de dados completo.", "probabilitySplineChartToggleLabel": "Usar o gráfico spline", "countAxisLabel": "Contagem", @@ -1685,76 +1687,76 @@ "flyoutDescription": "Você pode optar por exibir coortes de conjuntos de dados ou coortes de recursos. Se as coortes de recursos não estiverem disponíveis, primeiro você precisará selecionar um ou mais recursos na exibição de coortes de recursos. Posteriormente, as coortes de recursos serão geradas e você poderá selecioná-las aqui." }, "regressionTargetOptions": { - "predictedY": "Predicted Y", - "trueY": "True Y", - "error": "Error" + "predictedY": "Y Previsto", + "trueY": "Y Verdadeiro", + "error": "Erro" }, "topLevelDescription": "Avalie o desempenho do modelo explorando a distribuição dos valores de previsão e os valores das métricas de desempenho do modelo. Use a guia \"Coortes do conjunto de dados\" para investigar seu modelo analisando uma análise consistente de seu desempenho em diferentes coortes de conjuntos de dados pré-criados ou recém-criados. Use os \"Coortes de recursos\" para investigar seu modelo analisando uma análise consistente de seu desempenho entre subconjuntos de recursos confidenciais/não confidenciais. (por exemplo, desempenho em diferentes sexos, níveis de renda).", - "infoTitle": "Additional information on model overview", + "infoTitle": "Informações adicionais sobre a visão geral do modelo", "visualDisplayToggleLabel": "Mostrar mapa de calor", "featureBasedViewDescription": "Selecione até dois recursos para ver a divisão de desempenho do modelo entre coortes baseadas em recursos (se um recurso for selecionado) ou coortes intersecionais (se dois recursos forem selecionados)." }, "TableViewTab": { - "Heading": "View the dataset in a table format for all features and rows." + "Heading": "Exiba o conjunto de dados em um formato de tabela para todos os recursos e linhas." } }, "Forecasting": { - "target": "Target", - "whatIfForecastingHeader": "What-if analysis", - "forecastHeader": "Forecast analysis", - "whatIfForecastingDescription": "What-if allows you to perturb features for your entire time series and observe how the model's forecast changes.", - "whatIfForecastingChooseTimeSeries": "To start, choose a time series from the options below.", - "forecastDescription": "Forecast analysis compares your model's forecast to the actual values of your time series. To enable what-if analysis, provide a dataset with features.", - "timeSeries": "Time series", - "selectTimeSeries": "Select a time series.", - "singleTimeSeries": "The dataset contains only a single time series '{0}' which has been selected by default.", - "trueY": "True Y", - "baselinePrediction": "Baseline prediction", - "forecastComparisonHeader": "Compare What-if Forecasts", - "forecastComparisonChartTitle": "Forecasts", - "forecastComparisonChartTimeAxisLabel": "Time", + "target": "Destino", + "whatIfForecastingHeader": "Análise de Hipóteses", + "forecastHeader": "Análise de previsão", + "whatIfForecastingDescription": "O what-if permite que você altere recursos para toda a série temporal e observe como a previsão do modelo muda.", + "whatIfForecastingChooseTimeSeries": "Para começar, escolha uma série temporal nas opções abaixo.", + "forecastDescription": "A análise de previsão compara a previsão do modelo com os valores reais da série temporal. Para habilitar a análise de hipóteses, forneça um conjunto de dados com recursos.", + "timeSeries": "Série temporal", + "selectTimeSeries": "Selecione uma série temporal.", + "singleTimeSeries": "O conjunto de dados contém apenas uma única série temporal \"{0}\" que foi selecionada por padrão.", + "trueY": "Y Verdadeiro", + "baselinePrediction": "Previsão de linha de base", + "forecastComparisonHeader": "Comparar previsões de what-if", + "forecastComparisonChartTitle": "Previsões", + "forecastComparisonChartTimeAxisLabel": "Tempo", "Transformations": { - "multiply": "multiply", - "divide": "divide", - "add": "add", - "subtract": "subtract", - "change": "change to" + "multiply": "multiplicar", + "divide": "dividir", + "add": "adicionar", + "subtract": "subtrair", + "change": "Alterar para:" }, "TransformationCreation": { - "title": "Create what-if scenario", - "nameLabel": "What-if scenario name", - "featureInstructions": "Choose a feature to perturb.", - "operationInstructions": "Choose an operation to apply to the feature.", - "operationDropdownHeader": "Operation", - "featureDropdownHeader": "Feature", - "valueSpinButtonHeader": "Value", - "scenarioNamingInstructionsPlaceholder": "Enter a unique name", - "scenarioNamingInstructions": "Enter a name for your what-if scenario.", - "scenarioNamingCollisionMessage": "This name exists already. Please enter a unique name.", - "scenarioNamingLengthMessage": "The name must be between 1 and 50 characters. The actual length is {0}.", - "scenarioNamingInvalidCharactersMessage": "The name can only contain alphanumeric characters, whitespaces, dashes, or underscores, and needs to start with an alphanumeric character.", - "valueErrorMessage": "For operation {0} please select a value other than {1}.", - "invalidCombinationErrorMessage": "This is identical to an existing what-if scenario. Please change the feature, operation, or value.", - "addTransformationButton": "Add Transformation", - "divisionAndMultiplicationBy": "by" + "title": "Criar cenário de what-if", + "nameLabel": "Nome do cenário what-if", + "featureInstructions": "Escolha um recurso a ser afetado.", + "operationInstructions": "Escolha uma operação a ser aplicada ao recurso.", + "operationDropdownHeader": "Operação", + "featureDropdownHeader": "Recurso", + "valueSpinButtonHeader": "Valor", + "scenarioNamingInstructionsPlaceholder": "Inserir um nome exclusivo", + "scenarioNamingInstructions": "Insira um nome para seu cenário de what-if.", + "scenarioNamingCollisionMessage": "Esse nome já existe. Insira um nome exclusivo.", + "scenarioNamingLengthMessage": "O nome deve ter entre 1 e 50 caracteres. O comprimento real é {0}.", + "scenarioNamingInvalidCharactersMessage": "O nome só pode conter caracteres alfanuméricos, espaços em branco, traços ou sublinhados e precisa começar com um caractere alfanumérico.", + "valueErrorMessage": "Para a operação {0}, selecione um valor diferente de {1}.", + "invalidCombinationErrorMessage": "Isso é idêntico a um cenário de what-if existente. Altere o recurso, a operação ou o valor.", + "addTransformationButton": "Adicionar transformação", + "divisionAndMultiplicationBy": "por" }, "TransformationTable": { - "nameColumnHeader": "Name", - "methodColumnHeader": "Method", - "divisionAndMultiplicationBy": "by ", - "header": "What-if Forecasts ({0})" + "nameColumnHeader": "Nome", + "methodColumnHeader": "Método", + "divisionAndMultiplicationBy": "por ", + "header": "Previsões de what-if ({0})" }, "TimeSeries": { - "apply": "Apply", - "cancel": "Cancel", - "cohortList": "Time series list", - "selectCohort": "Select a time series", - "shiftCohort": "Switch time series", - "shiftCohortDescription": "Select a time series from the time series list. Apply the time series to the dashboard." + "apply": "Aplicar", + "cancel": "Cancelar", + "cohortList": "Lista de séries temporais", + "selectCohort": "Selecione uma série temporal", + "shiftCohort": "Alternar série temporal", + "shiftCohortDescription": "Selecione uma série temporal na lista de séries temporais. Aplique a série temporal ao painel." }, "TimeSeriesSettings": { - "CohortSettingsDescription": "Time series are pre-defined based on time series identifying columns.", - "CohortSettingsTitle": "Time series settings" + "CohortSettingsDescription": "As séries temporais são predefinidas com base em colunas de identificação de série temporal.", + "CohortSettingsTitle": "Configurações da Série" } } } \ No newline at end of file diff --git a/libs/localization/src/lib/en.pt-PT.json b/libs/localization/src/lib/en.pt-PT.json index 562c3dba5e..121f29d777 100644 --- a/libs/localization/src/lib/en.pt-PT.json +++ b/libs/localization/src/lib/en.pt-PT.json @@ -906,6 +906,8 @@ "index": "Índice", "output": "Resultado", "predictedY": "Y Previsto", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Probabilidade: {0}", "trueY": "Y Verdadeiro", "xValue": "Valor de X:", @@ -1147,7 +1149,7 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Painel de interpretação", - "importantWords": "Show most important words", + "importantWords": "Mostrar Palavras Mais Importantes", "topFeatureList": "Análise da lista de características de topo", "allButton": "TODAS AS FUNCIONALIDADES", "negButton": "FUNCIONALIDADES NEGATIVAS", @@ -1162,7 +1164,7 @@ "trueAnswer": "Resposta verdadeira: ", "inputs": "Entradas", "outputs": "Saídas", - "sliderAriaLabel": "Slider for most important words" + "sliderAriaLabel": "Controlo de deslize para palavras mais importantes" }, "Legend": { "featureLegend": "LEGENDA DA FUNCIONALIDADE DE TEXTO", @@ -1195,8 +1197,8 @@ "columnTwo": "Índice", "columnThree": "Y Verdadeiro", "columnFour": "Y Previsto", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Correto", + "columnFourOD": "Incorreto", "columnFive": "Outros metadados", "chooseObject": "Escolha um objeto detetado", "examples": "exemplos", @@ -1216,8 +1218,8 @@ "panelInformation": "Informações", "predictedLabel": "Etiqueta prevista", "predictedY": "Previsto: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", + "correctDetections": "Deteções corretas: ", + "incorrectDetections": "Deteções incorretas: ", "prefix": "Objeto: ", "rows": "Linhas: ", "search": "Procurar", diff --git a/libs/localization/src/lib/en.ru.json b/libs/localization/src/lib/en.ru.json index c444b0b333..3769c0c0f1 100644 --- a/libs/localization/src/lib/en.ru.json +++ b/libs/localization/src/lib/en.ru.json @@ -3,26 +3,26 @@ "close": "Закрыть", "tooltipButton": "Кнопка подсказки", "identityFeature": "Функция удостоверения", - "infoTitle": "Additional information", - "spinButton": "Spin", - "editButton": "Edit", - "decreaseValue": "Decrease value", - "increaseValue": "Increase value", - "decreaseValueByOne": "Decrease value by 1", - "increaseValueByOne": "Increase value by 1", - "loading": "Loading..." + "infoTitle": "Дополнительные сведения", + "spinButton": "Вращать", + "editButton": "Изменить", + "decreaseValue": "Уменьшить значение", + "increaseValue": "Увеличить значение", + "decreaseValueByOne": "Уменьшить значение на 1", + "increaseValueByOne": "Увеличить значение на 1", + "loading": "Идет загрузка…" }, "ChartContextMenu": { - "hideData": "Hide data table", - "viewData": "View data table", - "viewInFullScreen": "View in full screen", - "printChart": "Print chart", - "downloadCSV": "Download CSV", - "downloadPNG": "Download PNG image", - "downloadJPEG": "Download JPEG image", - "downloadPDF": "Download PDF document", - "downloadSVG": "Download SVG vector image", - "downloadXLS": "Download XLS" + "hideData": "Скрыть таблицу данных", + "viewData": "Просмотреть таблицу данных", + "viewInFullScreen": "Просмотреть в полноэкранном режиме", + "printChart": "Распечатать диаграмму", + "downloadCSV": "Скачать CSV-файл", + "downloadPNG": "Скачать изображение в формате PNG", + "downloadJPEG": "Скачать изображение в формате JPEG", + "downloadPDF": "Скачать PDF-документ", + "downloadSVG": "Скачать векторное изображение SVG", + "downloadXLS": "Скачать XLS-файл" }, "CausalAnalysis": { "AggregateView": { @@ -39,7 +39,7 @@ "description": "Анализ причинно-следственных связей дает ответы на вопросы \"что если\" о том, как бы изменились реальные результаты при выборе другого варианта политики, например при применении другой стратегии ценообразования для продукта или в случае использования альтернативного лечения пациента. В отличии от прогнозов модели, определяющих важные шаблоны корреляции, эти средства помогают определить наиболее важные признаки, которые непосредственно влияют на интересующие результаты. Эти модели определяют причинно-следственный эффект одного признака (обычно называемый \"обработкой\") при постоянстве других несмешанных признаков. Чтобы достичь наилучших результатов, убедитесь, что полный набор данных содержит все доступные признаки, которые могут коррелировать с результатом как искажающие результат факторы.", "directAggregate": "Прямой агрегированный причинно-следственный эффект каждой обработки с доверительным интервалом 95%", "here": "здесь", - "infoTitle": "Additional information on aggregated causal effects", + "infoTitle": "Подробнее об агрегированных эффектах причины и следствия", "lasso": "Инструмент \"лассо\" (или логистическая регрессия, если y двоичное) предназначен для прогнозирования y на основе X[-i], а также (логистическая регрессия, если X[i] категориальное) для прогнозирования X[i] на основе X[-i]. Причинно-следственное влияние можно представить в виде корреляции остаточных или необъясненных вариантов двух задач прогнозирования. Дополнительные сведения о двойном машинном обучении", "unconfounding": "Что такое искажающие признаки?" }, @@ -51,7 +51,7 @@ "description": "Отдельные причинно-следственные эффекты могут давать информацию для персонализированной обработки, такой как целевое продвижение среди клиентов или индивидуальный план лечения. Как именно лицо с определенным набором признаков будет реагировать на изменение причинного признака или лечения? Причинно-следственный инструмент \"что-если\" вычисляет незначительные изменения в реальных результатах для конкретного лица, если уровень обработки для него будет изменен. Этот анализ позволяет понять, как изменились бы реальные результаты при выборе разных вариантов политики, например, другой стратегии ценообразования продукта или альтернативного лечения пациента. Укажите интересующую обработку и посмотрите, как изменится реальный результат.", "directIndividual": "Прямой индивидуальный причинно-следственный эффект каждой обработки с доверительным интервалом 95%", "index": "Индекс точки данных", - "infoTitle": "Additional information on individual causal what-if", + "infoTitle": "Подробнее о конкретной причинно-следственной ошибке \"что если\"", "missingParameters": "На этой вкладке требуется указать набор данных для оценки.", "newOutcome": "Новый результат", "selectTreatment": "Выбор обработки", @@ -85,7 +85,7 @@ "averageGainBinary": "Средний выигрыш от приведения обработки {0} к базовому значению {1}.", "averageGainContinuous": "Среднее преимущество альтернативных политик по сравнению с обработкой без {0}.", "header": "Эти инструменты помогают вырабатывать политики для будущей обработки. Вы можете определить, какие части выборки испытывают наиболее сильные отклики на изменения в причинных признаках или обработках, и составить правила, определяющие, на какие совокупности в будущем следует нацеливать конкретную обработку.", - "infoTitle": "Additional information on treatment policy", + "infoTitle": "Подробнее о политике обработки", "nSample": "n = {0}", "noData": "Нет данных" } @@ -116,8 +116,8 @@ "cancel": "Отмена", "title": "Переключить когорту", "subText": "Выберите когорту из списка когорт. Примените когорту к панели мониторинга.", - "selectCohort": "Select a cohort", - "cohortList": "Cohort list" + "selectCohort": "Выберите когорту", + "cohortList": "Список когорт" }, "PreBuiltCohort": { "featureNameNotFound": "Имя функции не найдено в наборе данных", @@ -148,13 +148,13 @@ "predictedClass": "Спрогнозированный класс", "predictedValue": "Спрогнозированное значение" }, - "Size": "Size", - "loading": "Loading...", + "Size": "Размер", + "loading": "Идет загрузка…", "counterfactualEx": "Пример, противоречащий фактам {0}", "counterfactualName": "Название противоречащего фактам предположения для анализа \"что если\"", "createWhatIfCounterfactual": "Создать противоречащее фактам предположение для анализа \"что если\"", "createCounterfactual": "Противоречащее фактам предположение", - "revertToBubbleChart": "View bubble chart", + "revertToBubbleChart": "Просмотреть пузырьковую диаграмму", "createOwn": "Создайте собственное предположение, противоречащее фактам:", "currentClass": "Текущий класс", "currentRange": "Текущий диапазон", @@ -167,9 +167,9 @@ "listDescription": "Этот список показывает, какие точки данных в выбранном образце данных показывают наибольший отклик на выбранную обработку, на основе всех признаков, включенных в оцениваемую причинно-следственную модель. Пять столбцов слева показывают, рекомендована ли обработка для наблюдения, текущую обработку, предполагаемый эффект обработки (эффект от применения обработки по сравнению с отсутствием обработки для двоичных обработок или увеличением/уменьшением признака обработки на 10% типового размера обработки в выборке [в динамике: отображается численное изменение в используемой обработки]), а также нижний и верхний доверительные интервалы (CI) для этого эффекта. В остальных столбцах показаны текущее состояние обработки и другие признаки каждого наблюдения.", "localImportanceDescription": "Признаки высшего ранга в строке {0}, которые надо изменить для достижения желаемого прогноза модели. На основе анализа \"что если\" для прогноза: {1}", "localImportanceSelectData": "Выберите точку данных, чтобы просмотреть локальную диаграмму важности", - "largeLocalImportanceSelectData": "Select a bubble, followed by a data point to view local importance chart", - "localImportanceFetchError": "There was an error while fetching the local importance data. Error details: {0} Please check the data used.", - "BubbleChartFetchError": "There was an error while fetching the data. Error details: {0} Please check the data used.", + "largeLocalImportanceSelectData": "Выберите пузырек, а затем точку данных, чтобы увидеть диаграмму локальной важности", + "localImportanceFetchError": "Произошла ошибка при получении данных локальной важности. Сведения об ошибке: {0}. Проверьте использованные данные.", + "BubbleChartFetchError": "Произошла ошибка при получении данных. Сведения об ошибке: {0}. Проверьте использованные данные.", "noData": "Нет данных", "noFeatures": "Нет доступных признаков", "panelDescription": "Просматривайте предположения, противоречащие фактам, и создавайте собственные. Выполняйте поиск по признакам, чтобы просматривать предлагаемые значения из широкого набора примеров предположений, противоречащих фактам. Устанавливайте предлагаемые значения признаков для противоречащих фактам предположений, нажимая на ссылку «Установить значения» под названием каждого предположения. Указывайте названия для предположений, противоречащих фактам, и сохраняйте их.", @@ -223,13 +223,13 @@ "subText": "Ознакомьтесь с выбранной когортой. Измените имя когорты. Удалите эту когорту." }, "FeatureList": { - "featureList": "Feature List", + "featureList": "Список признаков", "apply": "Применить", "features": "Признаки", "importances": "Важность", "treeMapDescription": "Чтобы повторно обучить карту дерева, выберите и сохраните признаки ниже. Важность признаков была рассчитана с использованием взаимной информации с ошибкой в истинных метках. Используйте это в качестве руководства по обучению карты дерева.", "staticTreeMapDescription": "Просмотрите функции, которые использовались для обучения карты дерева. Важность признаков рассчитывалась с использованием взаимной информации с ошибкой на истинных метках.", - "searchResultMessage": "Results displayed out of {resultLength} for {searchValue}" + "searchResultMessage": "Показано результатов поиска {searchValue} из общего количества {resultLength}" }, "TreeViewParameters": { "maximumDepth": "Максимальная глубина", @@ -295,7 +295,7 @@ "disabledWarning": "Тепловая карта ошибок отключена, если глобальная когорта не переключена на представление всех данных, так как она создается для полного набора данных. Вернитесь к полному набору данных, чтобы просмотреть тепловую карту ошибок." }, "MatrixSummary": { - "heatMapInfoTitle": "Additional information on heat map", + "heatMapInfoTitle": "Подробнее о тепловой карте", "heatMapDescription": "С помощью тепловой карты можно сфокусироваться на определенных фильтрах пересечения признаков и вычислить разъединенные коэффициенты ошибок. Начните с двух признаков набора данных для сравнения.", "heatMapStaticDescription": "Тепловая карта позволяет сосредоточиться на фильтрах определенных пересекающихся признаков и вычислить частоту дезагрегированных ошибок. Чтобы создать тепловую карту с помощью пакета SDK, необходимо выбрать один или два признака перед просмотром панели мониторинга." }, @@ -311,108 +311,108 @@ }, "Metrics": { "AccuracyScore": { - "Name": "Accuracy score", - "Info": "The accuracy score represents the ratio of correct to total instances in the data.", - "Short": "Accuracy", - "Title": "Additional information on accuracy score" + "Name": "Оценка точности", + "Info": "Оценка точности представляет отношение правильных к общему количеству экземпляров в данных.", + "Short": "Правильность", + "Title": "Дополнительные сведения об оценке точности" }, "ErrorRate": { - "Name": "Error rate", - "Info": "The error rate represents the percentage of instances in the node for which the system has failed.", - "Short": "Error rate", - "Title": "Additional information on error rate" + "Name": "Частота ошибок", + "Info": "Частота ошибок представляет процент экземпляров в узле, для которого произошла ошибка системы.", + "Short": "Частота ошибок", + "Title": "Дополнительные сведения о частоте ошибок" }, "F1Score": { - "Name": "F1 score", - "Info": "The F1 score is the harmonic mean of the precision and recall metrics.", - "Short": "F1 score", - "Title": "Additional information on F1 score" + "Name": "Показатель F1", + "Info": "Показатель F1 — это среднее гармоническое метрик точности и полноты.", + "Short": "Показатель F1", + "Title": "Дополнительные сведения о показателе F1" }, "MeanAbsoluteError": { - "Name": "Mean absolute error", - "Info": "The mean absolute error is the average of the sum of the errors.", - "Short": "Mean abs. error", - "Title": "Additional information on mean absolute error" + "Name": "Средняя абсолютная погрешность", + "Info": "Средняя абсолютная погрешность — это среднее значение суммы ошибок.", + "Short": "Средняя абсолютная погрешность", + "Title": "Дополнительные сведения о средней абсолютной погрешности" }, "MeanSquaredError": { - "Name": "Mean squared error", - "Info": "The mean squared error is the average of the squares of the errors.", - "Short": "Mean sq. error", - "Title": "Additional information on mean squared error" + "Name": "Среднеквадратическая погрешность", + "Info": "Среднеквадратическая погрешность — это среднее значение квадратов ошибок.", + "Short": "Среднеквадратическая погрешность", + "Title": "Дополнительные сведения о среднеквадратической погрешности" }, "Precision": { - "Name": "Precision score", - "Info": "The precision is the ratio of true positives over all predicted positives.", - "Short": "Precision", - "Title": "Additional information on precision" + "Name": "Оценка точности", + "Info": "Точность — это отношение истинноположительных результатов ко всем прогнозируемым положительным результатам.", + "Short": "Точность", + "Title": "Дополнительные сведения о точности" }, "Recall": { - "Name": "Recall score", - "Info": "The recall is the ratio of true positives over all actual positives.", - "Short": "Recall", - "Title": "Additional information on recall" + "Name": "Оценка полноты", + "Info": "Полнота — это отношение истинноположительных результатов ко всем фактическим положительным результатам.", + "Short": "Полнота", + "Title": "Дополнительные сведения о полноте" }, "MacroPrecision": { - "Name": "Macro averaged precision score", - "Info": "The macro averaged precision is the ratio of true positives over all predicted positives computed independently per class and averaged.", - "Short": "Macro precision", - "Title": "Additional information on macro averaged precision" + "Name": "Оценка макро-усредненной точности", + "Info": "Макро-усредненная точность — это отношение истинноположительных результатов ко всем прогнозируемым положительным результатам, вычисляемое независимо для каждого класса и усредненное.", + "Short": "Макро-точность", + "Title": "Дополнительные сведения о макро-усредненной точности" }, "MicroPrecision": { - "Name": "Micro averaged precision score", - "Info": "The micro averaged precision is the ratio of true positives over all predicted positives aggregated for all classes.", - "Short": "Micro precision", - "Title": "Additional information on micro averaged precision" + "Name": "Оценка микро-усредненной точности", + "Info": "Микро-усредненная полнота — это отношение истинноположительных результатов ко всем прогнозируемым положительным результатам, агрегированное для всех классов.", + "Short": "Микро-точность", + "Title": "Дополнительные сведения о микро-усредненной точности" }, "MacroRecall": { - "Name": "Macro averaged recall score", - "Info": "The macro averaged recall is the ratio of true positives over all actual positives computed independently per class and averaged.", - "Short": "Macro recall", - "Title": "Additional information on macro averaged recall" + "Name": "Оценка макро-усредненной полноты", + "Info": "Макро-усредненная полнота — это отношение истинноположительных результатов ко всем фактическим положительным результатам, вычисляемое независимо для каждого класса и усредненное.", + "Short": "Макро-полнота", + "Title": "Дополнительные сведения о макро-усредненной полноте" }, "MicroRecall": { - "Name": "Micro averaged recall score", - "Info": "The micro averaged recall is the ratio of true positives over all actual positives aggregated for all classes.", - "Short": "Micro recall", - "Title": "Additional information on micro averaged recall" + "Name": "Оценка микро-усредненной полноты", + "Info": "Микро-усредненная полнота — это отношение истинноположительных результатов ко всем фактическим положительным результатам, агрегированное для всех классов.", + "Short": "Микро-полнота", + "Title": "Дополнительные сведения о микро-усредненной полноте" }, "MacroF1Score": { - "Name": "Macro averaged F1 score", - "Info": "The macro averaged F1 score is the harmonic mean of the macro averaged precision and recall metrics.", - "Short": "Macro F1 score", - "Title": "Additional information on macro averaged F1 score" + "Name": "Макроусредненный показатель F1", + "Info": "Макроусредненный показатель F1 представляет собой гармоническое среднее макроусредненных метрик точности и полноты.", + "Short": "Макро показатель F1", + "Title": "Дополнительные сведения о макро-усредненном показателе F1" }, "MicroF1Score": { - "Name": "Micro averaged F1 score", - "Info": "The micro averaged F1 score is the harmonic mean of the micro averaged precision and recall metrics.", - "Short": "Micro F1 score", - "Title": "Additional information on micro averaged F1 score" + "Name": "Микроусредненный показатель F1", + "Info": "Микроусредненный показатель F1 представляет собой гармоническое среднее микроусредненных метрик точности и полноты.", + "Short": "Микро показатель F1", + "Title": "Дополнительные сведения о микро-усредненном показателе F1" }, "MeanAveragePrecision": { - "Name": "Mean average precision score", - "Info": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall.", - "Short": "Mean avg precision", - "Title": "Additional information on mean average precision score" + "Name": "Оценка именованной средней точности", + "Info": "Средняя точность для моделей обнаружения объектов — это среднее значение AP (средней точности) для всех классов. Этот показатель оценивает надежность модели обнаружения объектов и инкапсулирует переключение между точностью и отзывом.", + "Short": "Именованная средняя точность", + "Title": "Дополнительные сведения об оценке именованной средней точности" }, "AveragePrecision": { - "Name": "Average precision score", - "Info": "Average precision for object detection models is calculated for a selected class.", - "Short": "Avg precision", - "Title": "Additional information on average precision score" + "Name": "Средний показатель точности", + "Info": "Средняя точность для моделей обнаружения объектов вычисляется для выбранного класса.", + "Short": "Средняя точность", + "Title": "Дополнительные сведения о среднем показателе точности" }, "AverageRecall": { - "Name": "Average recall score", - "Info": "Average recall for object detection models is calculated for a selected class.", - "Short": "Avg recall", - "Title": "Additional information on average recall score" + "Name": "Средняя оценка полноты", + "Info": "Средняя полнота для моделей обнаружения объектов вычисляется для выбранного класса.", + "Short": "Средняя полнота", + "Title": "Дополнительные сведения о средней оценке полноты" }, "metricName": "Имя метрики", "metricValue": "Значение метрики" }, "MetricSelector": { "selectorLabel": "Выбрать метрику", - "feature1SelectorLabel": "Rows: Feature 1", - "feature2SelectorLabel": "Columns: Feature 2" + "feature1SelectorLabel": "Строки: признак 1", + "feature2SelectorLabel": "Столбцы: признак 2" }, "Navigation": { "cohortSaved": "Новая когорта сохранена. См. список когорт в параметрах когорты.", @@ -433,9 +433,9 @@ "defaultLabelCopy": "Копировать все данные" }, "TreeView": { - "ariaLabel": "Interactive chart", - "disabledArialLabel": "Disabled interactive chart", - "treeMapInfoTitle": "Additional information on tree map", + "ariaLabel": "Интерактивная диаграмма", + "disabledArialLabel": "Деактивированная интерактивная диаграмма", + "treeMapInfoTitle": "Подробнее о древовидной карте", "treeDescription": "Визуализация дерева использует взаимную информацию между каждым признаком и ошибкой, чтобы оптимальным образом иерархически отделять экземпляры ошибок от успешных экземпляров в данных. Это упрощает процесс обнаружения и выделения закономерностей распространенных сбоев. Чтобы отредактировать список используемых функций в дереве, нажмите «Список функций». Используйте раскрывающееся меню «Выбор метрики», чтобы узнать больше о производительности узлов ошибок и успехов. Обратите внимание, что этот выбор метрики не повлияет на способ создания вашего дерева ошибок.", "treeStaticDescription": "Визуализация дерева использует взаимную информацию между каждым признаком и ошибкой, чтобы оптимальным образом иерархически отделять экземпляры ошибок от успешных экземпляров в данных. Это упрощает процесс обнаружения и выделения закономерностей распространенных сбоев. Чтобы найти важные шаблоны отказов, ищите узлы с более ярким красным цветом (т. е. с высокой частотой ошибок) и более высокой линией заполнения (т. е. с высоким покрытием ошибок). Чтобы просмотреть список функций, использованных при создании этого дерева ошибок, нажмите «Список функций». Используйте раскрывающееся меню «Выбор метрики», чтобы узнать больше о производительности узлов ошибок и успехов. Обратите внимание, что этот выбор метрики не повлияет на способ создания вашего дерева ошибок.", "disabledWarning": "Карта дерева ошибок отключена, если глобальная когорта не переключена на представление всех данных, так как она создается для полного набора данных. Вернитесь к полному набору данных, чтобы просмотреть карту дерева ошибок." @@ -770,7 +770,7 @@ "countHelperText": "Гистограмма количества точек", "ditherLabel": "Размывание значений", "groupByCohort": "Группировать по когорте", - "logarithmicScaling": "Enable logarithmic scaling", + "logarithmicScaling": "Включить логарифмическую шкалу", "numOfBins": "Число интервалов", "selectClass": "Выберите класс", "selectFeature": "Выберите признак", @@ -794,7 +794,7 @@ "importancePrefix": "Важность", "numberOfDatapoints": "Количество точек данных", "rowIndex": "Индекс строки", - "absoluteIndex": "Absolute index", + "absoluteIndex": "Абсолютный индекс", "xValue": "Значение X", "yValue": "Значение Y" }, @@ -822,12 +822,12 @@ }, "CohortEditor": { "columns": { - "index": "Index", - "dataset": "Dataset", - "predictedY": "Predicted Y", - "trueY": "True Y", - "classificationOutcome": "Classification outcome", - "regressionError": "Error" + "index": "Индекс", + "dataset": "Набор данных", + "predictedY": "Спрогнозированное значение Y", + "trueY": "Истинное значение Y", + "classificationOutcome": "Результат классификации", + "regressionError": "Ошибка" }, "TreatAsCategorical": "Рассматривать как категориальные", "addFilter": "Добавить фильтр", @@ -852,8 +852,8 @@ "save": "Сохранить", "saveAndSwitch": "Сохранить и переключить", "selectFilter": "Выберите фильтр", - "noFiltersApplied": "No filters applied", - "filterAdded": "Filter added" + "noFiltersApplied": "Ни один фильтр не применен.", + "filterAdded": "Фильтр добавлен." }, "Columns": { "classificationOutcome": "Результат классификации", @@ -863,8 +863,8 @@ "falsePositive": "Ложноположительный результат", "none": "Количество", "predictedProbabilities": "Прогнозируемые вероятности", - "predictedLabels": "Predicted labels", - "trueLabels": "True labels", + "predictedLabels": "Прогнозируемые метки", + "trueLabels": "Истинные метки", "regressionError": "Ошибка регрессии", "trueNegative": "Истинный отрицательный результат", "truePositive": "Истинноположительный результат", @@ -885,7 +885,7 @@ "aggregatePlots": "Агрегированные графики", "chartType": "Тип диаграммы", "colorValue": "Цвет значения", - "infoTitle": "Additional information on data analysis chart view", + "infoTitle": "Подробнее о графическом представлении анализа данных", "helperText": "Создание когорт набора данных для анализа статистики набора данных по таким фильтрам, как предсказанный результат, функции набора данных и группы ошибок. Чрезмерная или недостаточная представленность в наборе данных.", "individualDatapoints": "Отдельные точки данных", "missingParameters": "На этой вкладке требуется указать набор данных для оценки.", @@ -906,6 +906,8 @@ "index": "Индекс", "output": "Выходные данные", "predictedY": "Спрогнозированный Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Вероятность: {0}", "trueY": "Истинное значение Y", "xValue": "Значение X:", @@ -974,10 +976,10 @@ "dependencePlotHelperText": "Этот график зависимости показывает связь значений признаков с соответствующими значениями важности признаков.", "dependencePlotTitle": "Графики зависимостей", "helperText": "Изучите первые k важных функций, влияющих на общие прогнозы по вашей модели (это также называется \"глобальным объяснением\"). Используйте ползунок для отображения важности признаков в порядке убывания. Значения важности признаков всех когорт отображаются рядом друг с другом, и их можно отключить, выбрав когорту в условных обозначениях. Щелкните любой признак на графике, и ниже будет показано распределение плотности влияния выбранного признака на прогноз.", - "infoTitle": "Additional information on aggregate feature importance", + "infoTitle": "Подробнее о совокупной важности признаков", "legendHelpText": "Щелкайте элементы условных обозначений для включения или отключения когорт на графике.", "missingParameters": "На этой вкладке требуется указать параметр локальной важности признака.", - "sortByCohort": "Sort by cohort", + "sortByCohort": "Сортировать по когорте", "sortBy": "Сортировать по точке данных", "topAtoB": "{0} наиболее важных признаков", "viewDependencePlotFor": "Показать график зависимостей для:", @@ -1020,15 +1022,15 @@ }, "Statistics": { "accuracy": "Правильность: {0}", - "bleuScore": "Bleu score: {0}", - "bertScore": "Bert score: {0}", - "exactMatchRatio": "Exact match ratio: {0}", - "rougeScore": "Rouge Score: {0}", + "bleuScore": "Оценка BLEU: {0}", + "bertScore": "Оценка BERT: {0}", + "exactMatchRatio": "Доля точных совпадений: {0}", + "rougeScore": "Оценка ROUGE: {0}", "fnr": "Частота ложноотрицательных результатов: {0}", "fpr": "Частота ложноположительных результатов: {0}", - "hammingScore": "Hamming score: {0}", + "hammingScore": "Оценка Хэмминга: {0}", "meanPrediction": "Средний прогноз {0}", - "meteorScore": "Meteor Score: {0}", + "meteorScore": "Оценка METEOR: {0}", "mse": "Среднеквадратическая погрешность: {0}", "precision": "Точность: {0}", "rSquared": "R²: {0}", @@ -1036,10 +1038,10 @@ "selectionRate": "Скорость выбора: {0}", "mae": "Средняя абсолютная погрешность: {0}", "f1Score": "Показатель F1: {0}", - "samples": "Sample size: {0}", - "meanAveragePrecision": "Mean average precision: {0}", - "averagePrecision": "Average precision: {0}", - "averageRecall": "Average recall: {0}" + "samples": "Размер выборки: {0}", + "meanAveragePrecision": "Средняя точность среднего арифметического: {0}", + "averagePrecision": "Средняя точность: {0}", + "averageRecall": "Средняя полнота: {0}" }, "ValidationErrors": { "addFilters": "Добавьте фильтры", @@ -1147,30 +1149,30 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Панель интерпретируемости", - "importantWords": "Show most important words", + "importantWords": "Показать самые важные слова", "topFeatureList": "Анализ списка лучших функций", "allButton": "ВСЕ ФУНКЦИИ", "negButton": "ОТРИЦАТЕЛЬНЫЕ ФУНКЦИИ", "posButton": "ПОЛОЖИТЕЛЬНЫЕ ФУНКЦИИ", - "legendText": "Positive scalar feature importances represent the extent that the words were important towards the classification of your selected label, and negative scalar feature importances represent words that encouraged your model away from your selected label.", - "legendTextForQA": "The left text box and the bar chart display the predictions of the model. The right text box shows the feature importance associated with a selected token. Positive feature importances represent the extent that the words were important towards marking the selected token as the starting/ending position of the answer.", + "legendText": "Положительные скалярные значения важности признаков представляют степень важности слова для классификации выбранной метки, а отрицательные скалярные значения важности признаков относятся к словам, побуждающим модель отказаться от выбранной вами метки.", + "legendTextForQA": "В левом текстовом поле и на линейчатой диаграмме отображаются прогнозы модели. В правом текстовом поле показана важность признака, связанная с выбранным токеном. Положительные значения важности признаков указывают на то, насколько важными были слова для обозначения выбранной лексемы в качестве начальной или конечной позиции ответа.", "label": "Метка", "colon": ": ", - "startingPosition": "STARTING POSITION", - "endingPosition": "ENDING POSITION", - "predictedAnswer": "Predicted answer: ", - "trueAnswer": "True answer: ", - "inputs": "Inputs", - "outputs": "Outputs", - "sliderAriaLabel": "Slider for most important words" + "startingPosition": "НАЧАЛЬНАЯ ПОЗИЦИЯ", + "endingPosition": "КОНЕЧНАЯ ПОЗИЦИЯ", + "predictedAnswer": "Прогнозируемый ответ: ", + "trueAnswer": "Правильный ответ: ", + "inputs": "Входные данные", + "outputs": "Выходные данные", + "sliderAriaLabel": "Ползунок для самых важных слов" }, "Legend": { "featureLegend": "ЛЕГЕНДЫ ТЕКСТОВЫХ ФУНКЦИЙ", "posFeatureImportance": "ПОЛОЖИТЕЛЬНАЯ ВАЖНОСТЬ ФУНКЦИЙ", "negFeatureImportance": "ОТРИЦАТЕЛЬНАЯ ВАЖНОСТЬ ФУНКЦИИ", - "cls": "CLS: start of the sentence", - "sep": "SEP: end of the sentence", - "selectedWord": "Selected word: " + "cls": "CLS: начало предложения", + "sep": "SEP: конец предложения", + "selectedWord": "Выбранное слово: " }, "BarChart": { "featureImportance": "ВАЖНОСТЬ ФУНКЦИИ" @@ -1178,59 +1180,59 @@ }, "InterpretVision": { "Cohort": { - "close": "Close", - "errorCohortName": "Please choose a unique cohort name.", - "errorNumSelected": "Please select at least one (1) item.", - "itemsSelectedSingular": "item selected", - "itemsSelectedPlural": "items selected", - "save": "Save cohort", - "saveAndClose": "Save and close", - "saveAndSwitch": "Save and switch", - "textField": "New cohort name", - "title": "Save new cohort" + "close": "Закрыть", + "errorCohortName": "Выберите уникальное имя когорты.", + "errorNumSelected": "Выберите по крайней мере один элемент.", + "itemsSelectedSingular": "элемент выбран", + "itemsSelectedPlural": "выбрано элементов", + "save": "Сохранить когорту", + "saveAndClose": "Сохранить и закрыть", + "saveAndSwitch": "Сохранить и переключить", + "textField": "Новое имя когорты", + "title": "Сохранить новую когорту" }, "Dashboard": { "allData": "Все данные", - "columnOne": "Image", + "columnOne": "Изображение", "columnTwo": "Индекс", "columnThree": "Истинное значение Y", "columnFour": "Спрогнозированный Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Верно", + "columnFourOD": "Неверно", "columnFive": "Другие метаданные", - "chooseObject": "Choose a detected object", - "examples": "examples", + "chooseObject": "Выберите обнаруженный объект", + "examples": "примеры", "filter": "Фильтр", - "indexLabel": "Image ", - "labelTypeDropdown": "Select label type", - "labelVisibilityDropdown": "Select labels to display", - "legendFailure": "failure", - "legendSuccess": "success", - "loading": "Computing explanation for index", - "multiselect": "Multiselect", - "notdefined": "object scenario not defined", - "objectSelect": "Object Selection", + "indexLabel": "Изображение ", + "labelTypeDropdown": "Выбрать тип метки", + "labelVisibilityDropdown": "Выберите метки для показа", + "legendFailure": "сбой", + "legendSuccess": "успешно", + "loading": "Производится вычисление объяснения для индекса", + "multiselect": "Множественный выбор", + "notdefined": "сценарий объекта не определен", + "objectSelect": "Выбор объектов", "pageSize": "Размер страницы: ", - "panelTitle": "Selected instance", - "panelExplanation": "Explanation", - "panelInformation": "Information", - "predictedLabel": "Predicted label", - "predictedY": "Predicted: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", - "prefix": "Object: ", - "rows": "Rows: ", + "panelTitle": "Выбранный экземпляр", + "panelExplanation": "Объяснение", + "panelInformation": "Сведения", + "predictedLabel": "Спрогнозированная метка", + "predictedY": "Спрогнозировано: ", + "correctDetections": "Правильные обнаружения: ", + "incorrectDetections": "Неправильные обнаружения: ", + "prefix": "Объект: ", + "rows": "Строк: ", "search": "Поиск", - "selectAll": "Select all", + "selectAll": "Выделить все", "settings": "Параметры", - "showAll": "Show all", + "showAll": "Показать все", "tabOptionFirst": "Представление обозревателя изображений", "tabOptionSecond": "Представление таблицы", - "tabOptionThird": "Class view", + "tabOptionThird": "Представление классов", "thumbnailSize": "Размер эскиза", "titleBarError": "Экземпляры ошибок", "titleBarSuccess": "Успешные экземпляры", - "trueY": "Ground truth: " + "trueY": "Подлинная правда: " } }, "ModelAssessment": { @@ -1239,15 +1241,15 @@ "CalloutContent": "Добавление некоторых компонентов (представления ошибок в виде дерева, тепловой карты ошибок) позволит отфильтровать данные из глобальной когорты, которые отображаются в списке компонентов ниже.", "CalloutTitle": "Добавление компонента", "TabAddedMessage": { - "DataAnalysis": "Data analysis component added", - "FeatureImportances": "Feature importances component added", - "ErrorAnalysis": "Error analysis component added", - "Fairness": "Fairness component added", - "ModelOverview": "Model overview component added", - "CausalAnalysis": "Causal analysis component added", - "Counterfactuals": "Counterfactuals component added", - "Vision": "Vision data explorer component added", - "Forecasting": "Forecasting what-if component added" + "DataAnalysis": "Добавлен компонент анализа данных", + "FeatureImportances": "Добавлен компонент важности признаков", + "ErrorAnalysis": "Добавлен компонент анализа ошибок", + "Fairness": "Добавлен компонент справедливости", + "ModelOverview": "Компонент обзора модели добавлен", + "CausalAnalysis": "Добавлен компонент причинно-следственного анализа данных", + "Counterfactuals": "Добавлен счетчик фактов, противоречащих предположениям", + "Vision": "Компонент визуального распознавания обозревателя данных добавлен", + "Forecasting": "Добавлен компонент прогнозирования \"что если\"" } }, "CausalAnalysis": { @@ -1275,7 +1277,7 @@ }, "CohortInformation": { "ShiftCohort": "Переключить когорту", - "SwitchTimeSeries": "Switch time series", + "SwitchTimeSeries": "Переключить временные ряды", "NewCohort": "Новая когорта", "DataPoints": "Количество точек данных", "DefaultCohort": " (по умолчанию)", @@ -1287,7 +1289,7 @@ "CohortSettingsTitle": "Параметры когорты" }, "ComponentNames": { - "ChartView": "Chart view", + "ChartView": "Представление диаграммы", "CausalAnalysis": "Анализ причинно-следственных связей", "Counterfactuals": "Контрфактические предположения", "DataAnalysis": "Анализ данных", @@ -1296,10 +1298,10 @@ "ErrorAnalysis": "Анализ ошибок", "Fairness": "Справедливость", "FeatureImportances": "Важность признаков", - "Forecasting": "Forecasting", + "Forecasting": "Прогнозирование", "ModelOverview": "Обзор модели", - "TableView": "Table view", - "VisionTab": "Vision data explorer" + "TableView": "Табличное представление", + "VisionTab": "Открыть обозреватель данных" }, "DashboardSettings": { "Content": "В этом списке показан макет панели мониторинга. Данные можно фильтровать с помощью компонента анализа ошибок для отображения в списке компонентов ниже.", @@ -1458,16 +1460,16 @@ "GlobalExplanation": "Совокупная важность признаков", "IncorrectPredictions": "Неправильные прогнозы", "InfoTitle": "Additional information on feature importance values", - "IndividualFeatureTabular": "Select a datapoint by clicking on a datapoint (up to 5 datapoints) in the table to view their local feature importance values (local explanation) and individual conditional expectation (ICE) plots.", + "IndividualFeatureTabular": "Выберите точку данных, щелкнув ее (до 5 точек данных) в таблице, чтобы просмотреть локальные значения важности признаков (локальное объяснение) и графики отдельного условного ожидания (ICE).", "IndividualFeatureText": "Select a datapoint by clicking on a datapoint in the table to view the local feature importance values (local explanation) from SHAP's text explainer.", "LocalExplanation": "Важность отдельных признаков", "SelectionCounter": "Выбрано {0}/{1} точек данных", "SelectionLimit": "Сейчас можно выбрать до 5 точек данных.", - "RowCheckboxAriaLabel": "Row checkbox", - "SelectionColumnAriaLabel": "Toggle selection" + "RowCheckboxAriaLabel": "Флажок строки", + "SelectionColumnAriaLabel": "Переключить выбор" }, "IndividualFeatureImportanceView": { - "SmallInstanceSelection": "Instance selection" + "SmallInstanceSelection": "Выбор экземпляра" }, "MainMenu": { "DashboardSettings": "Конфигурация панели мониторинга", @@ -1483,44 +1485,44 @@ "ModelOverview": { "metrics": { "accuracy": { - "name": "Accuracy score", + "name": "Оценка точности", "description": "Доля точек данных с корректной классификацией." }, "exactMatchRatio": { - "name": "Exact match ratio", - "description": "The ratio of instances classified correctly for every label." + "name": "Доля точных совпадений", + "description": "Доля правильно классифицированных экземпляров для каждой метки." }, "meteorScore": { - "name": "Meteor Score", - "description": "METEOR Score is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision in question answering task." + "name": "Оценка METEOR", + "description": "Оценка METEOR вычисляется на основе гармонического среднего значения точности и полноты при ответе на вопрос, с использованием более высокого весового коэффициента для полноты, чем для точности." }, "bleuScore": { - "name": "Bleu Score", - "description": "Bleu Score measures the ratio of words (and/or n-grams) in the machine generated text that appeared in the reference text in question answering task." + "name": "Оценка BLEU", + "description": "Оценка BLEU измеряет долю слов и(или) n-грамм из сгенерированного компьютером текста, включенных в справочный текст при ответе на вопрос." }, "bertScore": { - "name": "Bert Score", - "description": "BERTScore focuses on computing semantic similarity between tokens of reference and machine generated text in question answering task." + "name": "Оценка BERT", + "description": "Оценка BERT направлена на вычисление семантического подобия между ссылочными токенами и текстом, который был сгенерирован компьютером в качестве ответа на вопрос." }, "rougeScore": { - "name": "Rouge Score", - "description": "Rouge Score measures the ratio of words (and/or n-grams) in the reference text that appeared in the machine generated text in question answering task." + "name": "Оценка ROUGE", + "description": "Оценка ROUGE измеряет долю слов и(или) n-грамм из справочного текста, включенных в сгенерированный компьютером текст при ответе на вопрос." }, "hammingScore": { - "name": "Hamming score", - "description": "The average ratio of labels classified correctly among those classified as 1 in multilabel task." + "name": "Оценка Хэмминга", + "description": "Средняя доля меток, классифицированных правильно, среди меток, классифицированных как 1 в задаче с несколькими метками." }, "f1Score": { "name": "Показатель F1", "description": "Показатель F1 — это среднее гармоническое точности и полноты." }, "f1ScoreMacro": { - "name": "Macro F1 score", - "description": "Macro F1 score is the harmonic mean of precision and recall for each class, with each class weighted equally." + "name": "Макро-оценка F1", + "description": "Макро-оценка F1 — это гармоническое среднее точности и полноты для каждого класса, полученное с использованием одинакового весового коэффициента для всех классов." }, "f1ScoreMicro": { - "name": "Micro F1 score", - "description": "Micro F1 score is the harmonic mean of precision and recall for each class, with each class weighted according to how many instances it contains." + "name": "Микро-оценка F1", + "description": "Микро-оценка F1 — это среднее гармоническое значение точности и полноты для каждого класса. Для классов используется весовой коэффициент в соответствии с количеством экземпляров в классе." }, "meanAbsoluteError": { "name": "Средняя абсолютная погрешность", @@ -1535,24 +1537,24 @@ "description": "Доля точек данных с корректной классификацией среди тех, что классифицированы как 1." }, "precisionMacro": { - "name": "Macro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted equally." + "name": "Макро-оценка точности", + "description": "Доля правильно классифицированных точек данных среди классифицированных как 1, для каждого класса. Для всех классов используются равные весовые коэффициенты." }, "precisionMicro": { - "name": "Micro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted according to how many instances it contains." + "name": "Микро-оценка точности", + "description": "Доля правильно классифицированных точек данных среди меток, классифицированных как 1, для каждого класса. Для классов используется весовой коэффициент в соответствии с количеством экземпляров в классе." }, "recall": { "name": "Оценка полноты", "description": "Доля точек данных с корректной классификацией среди тех, чья истинная метка — 1. Другие названия: доля истинноположительных результатов, чувствительность." }, "recallMacro": { - "name": "Macro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted equally." + "name": "Макро-оценка полноты", + "description": "Доля правильно классифицированных точек данных среди тех, у которых истинная метка равна 1, для каждого класса. Для всех классов используются равные весовые коэффициенты." }, "recallMicro": { - "name": "Micro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted according to how many instances it contains." + "name": "Макро-оценка полноты", + "description": "Доля правильно классифицированных точек данных среди тех, у которых истинная метка равна 1, для каждого класса. Для классов используется весовой коэффициент в соответствии с количеством экземпляров в классе." }, "falsePositiveRate": { "name": "Частота ложноположительных результатов", @@ -1571,32 +1573,32 @@ "description": "Среднее значение всех прогнозов." }, "meanAveragePrecision": { - "name": "Mean Average Precision score", - "description": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall." + "name": "Среднее значение среднего арифметического", + "description": "Средняя точность для моделей обнаружения объектов — это среднее значение AP (средней точности) для всех классов. Этот показатель оценивает надежность модели обнаружения объектов и инкапсулирует переключение между точностью и отзывом." }, "averagePrecision": { - "name": "Average Precision score", - "description": "Average precision for object detection models is calculated for a selected class." + "name": "Средний показатель точности", + "description": "Средняя точность для моделей обнаружения объектов вычисляется для выбранного класса." }, "averageRecall": { - "name": "Average Recall score", - "description": "Average recall for object detection models is calculated for a selected class." + "name": "Средняя оценка полноты", + "description": "Средняя полнота для моделей обнаружения объектов вычисляется для выбранного класса." }, "fairnessMetricDifference": "Разница", "fairnessMetricRatio": "Соотношение" }, "metricsDropdown": "Метрики", - "metricsTypeDropdown": "Aggregate method", + "metricsTypeDropdown": "Метод агрегирования", "metricTypes": { - "macro": "Macro", - "micro": "Micro" + "macro": "Макро", + "micro": "Микро" }, - "classSelectionDropdown": "Select class(es)", + "classSelectionDropdown": "Выберите класс(ы)", "iouThresholdDropdown": { - "name": "IoU Threshold", - "description": "Intersection over Union quantifies the degree of overlap between the prediction and ground truth bounding box of a detected object in an image. For example, setting an IoU threshold of 70% means that a prediction with greater than 70% overlap with ground truth is True, thus influencing the definition of prediction correctness and calculation of other performance metrics.", + "name": "Пороговое значение пересечения с объединением (IOU)", + "description": "Пересечение с объединением позволяет количественно измерить степень перекрытия между прогнозом и ограничивающим прямоугольником подлинной истинности обнаруженного объекта в изображении. Например, установка порога IoU в 70% означает, что истинным объявляется прогноз, перекрывающийся с подлинной истинностью на 70%. Это влияет на определение правильности прогноза и вычисление других метрик работы модели.", "iconId": "iouThresholdIconId", - "title": "Learn about the IoU threshold" + "title": "Подробнее о пороговом значении IOU" }, "notAvailable": "Н/Д", "countColumnHeader": "Размер выборки", @@ -1608,14 +1610,14 @@ "featuresDropdown": "Функции", "metricChartDropdownSelectionHeader": "Метрика", "probabilityForClassSelectionHeader": "Вероятность для класса", - "targetSelectionHeader": "Target", + "targetSelectionHeader": "Цель", "metricSelectionDropdownPlaceholder": "Выберите метрики для сравнения когорт.", - "classSelectionDropdownPlaceholder": "Select class name for class-based analysis.", + "classSelectionDropdownPlaceholder": "Выберите имя класса для анализа на основе класса.", "featureSelectionDropdownPlaceholder": "Выберите функции для анализа на основе функций.", "probabilityDistributionPivotItem": "Распределение вероятности", - "regressionDistributionPivotItem": "Target distribution", + "regressionDistributionPivotItem": "Целевое распределение", "metricsVisualizationsPivotItem": "Визуализации метрик", - "confusionMatrixPivotItem": "Confusion matrix", + "confusionMatrixPivotItem": "Матрица ошибок", "disaggregatedAnalysisFeatureSelectionPlaceholder": "Выберите компоненты для создания анализа на основе функций.", "tableCountTooltip": "Когорта {0} содержит {1} экземпляров.", "tableMetricTooltip": "{0} модели в когорте {1} — {2}", @@ -1626,36 +1628,36 @@ "metricSelectionButton": "Выберите метрику", "cohortSelectionButton": "Выберите когорты", "probabilityLabelSelectionButton": "Выбрать метку", - "regressionTargetSelectionButton": "Choose target", + "regressionTargetSelectionButton": "Выберите целевой объект", "selectAllCohortsOption": "Выделить все", "other": "Другое", "BoxPlot": { "outlierProbability": "вероятность", "outlierLabel": "Выбросы", "boxPlotSeriesLabel": "Блочная диаграмма", - "lowerWhisker": "Lower whisker", - "upperWhisker": "Upper whisker", - "median": "Median", - "lowerQuartile": "Lower quartile", - "upperQuartile": "Upper quartile" + "lowerWhisker": "Нижний ус", + "upperWhisker": "Верхний ус", + "median": "Медиана", + "lowerQuartile": "Нижний квартиль", + "upperQuartile": "Верхний квартиль" }, "chartConfigApply": "Применить", "chartConfigCancel": "Отмена", "chartConfigDatasetCohortSelectionPlaceholder": "Выберите когорты набора данных", "chartConfigFeatureBasedCohortSelectionPlaceholder": "Выберите когорты на основе функций", "confusionMatrix": { - "confusionMatrixCohortSelectionLabel": "Select dataset cohort", - "confusionMatrixClassSelectionLabel": "Select classes", - "confusionMatrixClassMinSelectionError": "Select at least {0} classes to visualize the confusion matrix.", - "confusionMatrixClassMaxSelectionError": "Select at most {0} classes to visualize the confusion matrix.", - "confusionMatrixClassSelectionDefaultPlaceholder": "Choose classes", - "confusionMatrixHeatmapTooltip": "{0} datapoints should be {1}, predicted to be {2}", - "confusionMatrixYAxisLabel": "True Class", - "confusionMatrixXAxisLabel": "Predicted Class", - "class": "Class" + "confusionMatrixCohortSelectionLabel": "Выберите когорту набора данных", + "confusionMatrixClassSelectionLabel": "Выбрать классы", + "confusionMatrixClassMinSelectionError": "Выберите не менее {0} классов, чтобы визуализировать матрицу ошибок.", + "confusionMatrixClassMaxSelectionError": "Для визуализации матрицы ошибок выберите не более {0} классов.", + "confusionMatrixClassSelectionDefaultPlaceholder": "Выберите классы", + "confusionMatrixHeatmapTooltip": "{0} точек данных должно быть {1}, согласно прогнозу - {2}", + "confusionMatrixYAxisLabel": "Истинный класс", + "confusionMatrixXAxisLabel": "Спрогнозированный класс", + "class": "Класс" }, "nA": "Н/Д", - "disaggregatedAnalysisBaseCohortDisclaimer": "The cohorts in the following feature-based analysis are based on the global cohort, {0}.", + "disaggregatedAnalysisBaseCohortDisclaimer": "Когорты в следующем функциональном анализе основаны на глобальной когорте {0}.", "disaggregatedAnalysisBaseCohortWarning": "В отличие от когорты {0}, {1} включает фильтры. Как следствие, он записывает только подмножество всего набора данных, и выводы могут не распространяться на весь набор данных.", "probabilitySplineChartToggleLabel": "Использовать сплайн-диаграмму", "countAxisLabel": "Количество", @@ -1685,76 +1687,76 @@ "flyoutDescription": "Вы можете просмотреть когорты наборов данных или когорты объектов. Если группы функций недоступны, вам необходимо сначала выбрать одну или несколько функций в представлении групп функций. Впоследствии создаются когорты функций, и вы можете выбрать их здесь." }, "regressionTargetOptions": { - "predictedY": "Predicted Y", - "trueY": "True Y", - "error": "Error" + "predictedY": "Спрогнозированное значение Y", + "trueY": "Истинное значение Y", + "error": "Ошибка" }, "topLevelDescription": "Оцените производительность модели, изучив распределение значений прогнозирования и значений метрик производительности модели. Используйте вкладку «Когорты наборов данных», чтобы изучить свою модель, просмотрев сравнительный анализ ее производительности для различных предварительно созданных или вновь созданных когорт наборов данных. Используйте «Когорты функций», чтобы исследовать свою модель, просматривая сравнительный анализ ее эффективности в подгруппах конфиденциальных и неконфиденциальных функций (например, производительность для разных полов и уровней дохода).", - "infoTitle": "Additional information on model overview", + "infoTitle": "Подробнее об обзоре моделей", "visualDisplayToggleLabel": "Показать тепловую карту", "featureBasedViewDescription": "Выберите до двух признаков, чтобы просмотреть разбивку производительности модели по когортам на основе признаков (если выбран один признак) или перекрестным когортам (если выбраны два признака)." }, "TableViewTab": { - "Heading": "View the dataset in a table format for all features and rows." + "Heading": "Просмотрите набор данных в виде таблицы для всех признаков и строк." } }, "Forecasting": { - "target": "Target", - "whatIfForecastingHeader": "What-if analysis", - "forecastHeader": "Forecast analysis", - "whatIfForecastingDescription": "What-if allows you to perturb features for your entire time series and observe how the model's forecast changes.", - "whatIfForecastingChooseTimeSeries": "To start, choose a time series from the options below.", - "forecastDescription": "Forecast analysis compares your model's forecast to the actual values of your time series. To enable what-if analysis, provide a dataset with features.", - "timeSeries": "Time series", - "selectTimeSeries": "Select a time series.", - "singleTimeSeries": "The dataset contains only a single time series '{0}' which has been selected by default.", - "trueY": "True Y", - "baselinePrediction": "Baseline prediction", - "forecastComparisonHeader": "Compare What-if Forecasts", - "forecastComparisonChartTitle": "Forecasts", - "forecastComparisonChartTimeAxisLabel": "Time", + "target": "Цель", + "whatIfForecastingHeader": "Анализ \"что если\"", + "forecastHeader": "Анализ прогноза", + "whatIfForecastingDescription": "Функция \"Что если\" позволяет менять значения признаков для всего временного ряда и наблюдать за изменением прогноза модели.", + "whatIfForecastingChooseTimeSeries": "Чтобы начать, выберите временной ряд из приведенного ниже списка.", + "forecastDescription": "Анализ прогнозов сравнивает прогноз модели с фактическими значениями временного ряда. Чтобы включить анализ \"что если\", предоставьте набор данных с признаками.", + "timeSeries": "Временной ряд", + "selectTimeSeries": "Выберите временной ряд.", + "singleTimeSeries": "Набор данных содержит только один временной ряд \"{0}\", и он выбран по умолчанию.", + "trueY": "Истинное значение Y", + "baselinePrediction": "Прогнозирование базовых показателей", + "forecastComparisonHeader": "Сравнить прогнозы \"что если\"", + "forecastComparisonChartTitle": "Прогнозы", + "forecastComparisonChartTimeAxisLabel": "Время", "Transformations": { - "multiply": "multiply", - "divide": "divide", - "add": "add", - "subtract": "subtract", - "change": "change to" + "multiply": "умножить", + "divide": "разделить", + "add": "добавить", + "subtract": "отнять", + "change": "изменить на" }, "TransformationCreation": { - "title": "Create what-if scenario", - "nameLabel": "What-if scenario name", - "featureInstructions": "Choose a feature to perturb.", - "operationInstructions": "Choose an operation to apply to the feature.", - "operationDropdownHeader": "Operation", - "featureDropdownHeader": "Feature", - "valueSpinButtonHeader": "Value", - "scenarioNamingInstructionsPlaceholder": "Enter a unique name", - "scenarioNamingInstructions": "Enter a name for your what-if scenario.", - "scenarioNamingCollisionMessage": "This name exists already. Please enter a unique name.", - "scenarioNamingLengthMessage": "The name must be between 1 and 50 characters. The actual length is {0}.", - "scenarioNamingInvalidCharactersMessage": "The name can only contain alphanumeric characters, whitespaces, dashes, or underscores, and needs to start with an alphanumeric character.", - "valueErrorMessage": "For operation {0} please select a value other than {1}.", - "invalidCombinationErrorMessage": "This is identical to an existing what-if scenario. Please change the feature, operation, or value.", - "addTransformationButton": "Add Transformation", - "divisionAndMultiplicationBy": "by" + "title": "Создать сценарий \"что если\"", + "nameLabel": "Имя сценария \"что если\"", + "featureInstructions": "Выберите признак, который будет меняться.", + "operationInstructions": "Выберите операцию для применения к признаку.", + "operationDropdownHeader": "Операция", + "featureDropdownHeader": "Признак", + "valueSpinButtonHeader": "Значение", + "scenarioNamingInstructionsPlaceholder": "Введите уникальное имя", + "scenarioNamingInstructions": "Введите имя сценария \"что если\".", + "scenarioNamingCollisionMessage": "Это имя уже существует. Введите уникальное имя.", + "scenarioNamingLengthMessage": "Имя должно содержать от 1 до 50 символов. Фактическая длина равна {0}.", + "scenarioNamingInvalidCharactersMessage": "Имя может содержать только буквы, цифры, пробелы, дефисы и символы подчеркивания и должно начинаться с буквы или цифры.", + "valueErrorMessage": "Для операции {0} выберите значение, отличное от {1}.", + "invalidCombinationErrorMessage": "Этот сценарий \"что если\" идентичен уже существующему. Измените признак, операцию или значение.", + "addTransformationButton": "Добавить преобразование", + "divisionAndMultiplicationBy": "на" }, "TransformationTable": { - "nameColumnHeader": "Name", - "methodColumnHeader": "Method", - "divisionAndMultiplicationBy": "by ", - "header": "What-if Forecasts ({0})" + "nameColumnHeader": "Имя", + "methodColumnHeader": "Метод", + "divisionAndMultiplicationBy": "на ", + "header": "Прогнозы \"что если\" ({0})" }, "TimeSeries": { - "apply": "Apply", - "cancel": "Cancel", - "cohortList": "Time series list", - "selectCohort": "Select a time series", - "shiftCohort": "Switch time series", - "shiftCohortDescription": "Select a time series from the time series list. Apply the time series to the dashboard." + "apply": "Применить", + "cancel": "Отмена", + "cohortList": "Список временных рядов", + "selectCohort": "Выберите временной ряд", + "shiftCohort": "Переключить временные ряды", + "shiftCohortDescription": "Выберите временной ряд из списка. Примените его к панели мониторинга." }, "TimeSeriesSettings": { - "CohortSettingsDescription": "Time series are pre-defined based on time series identifying columns.", - "CohortSettingsTitle": "Time series settings" + "CohortSettingsDescription": "Временные ряды определены задающими их столбцами.", + "CohortSettingsTitle": "Параметры временных рядов" } } } \ No newline at end of file diff --git a/libs/localization/src/lib/en.sv.json b/libs/localization/src/lib/en.sv.json index 6eac66066b..bf765a3680 100644 --- a/libs/localization/src/lib/en.sv.json +++ b/libs/localization/src/lib/en.sv.json @@ -906,6 +906,8 @@ "index": "Index", "output": "Utdata", "predictedY": "Förutsade Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Sannolikhet: {0}", "trueY": "Sant Y", "xValue": "X-värde:", @@ -1147,7 +1149,7 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Instrumentpanel för tolkning", - "importantWords": "Show most important words", + "importantWords": "Visa de viktigaste orden", "topFeatureList": "Analys av toppfunktionslistan", "allButton": "ALLA FUNKTIONER", "negButton": "NEGATIVA FUNKTIONER", @@ -1162,7 +1164,7 @@ "trueAnswer": "Sant svar: ", "inputs": "Indata", "outputs": "Utdata", - "sliderAriaLabel": "Slider for most important words" + "sliderAriaLabel": "Skjutreglage för de viktigaste orden" }, "Legend": { "featureLegend": "FÖRKLARING AV TEXTFUNKTION", @@ -1195,8 +1197,8 @@ "columnTwo": "Index", "columnThree": "Sant Y", "columnFour": "Förutsade Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Korrekt", + "columnFourOD": "Fel", "columnFive": "Andra metadata", "chooseObject": "Välj ett identifierat objekt", "examples": "exempel", @@ -1216,8 +1218,8 @@ "panelInformation": "Information", "predictedLabel": "Förväntad etikett", "predictedY": "Förutsagt: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", + "correctDetections": "Korrekta identifieringar: ", + "incorrectDetections": "Felaktiga identifieringar: ", "prefix": "Objekt: ", "rows": "Rader: ", "search": "Sök", diff --git a/libs/localization/src/lib/en.tr.json b/libs/localization/src/lib/en.tr.json index 041c2c18fc..01bd6c5111 100644 --- a/libs/localization/src/lib/en.tr.json +++ b/libs/localization/src/lib/en.tr.json @@ -3,26 +3,26 @@ "close": "Kapat", "tooltipButton": "Araç ipucu düğmesi", "identityFeature": "Kimlik özelliği", - "infoTitle": "Additional information", - "spinButton": "Spin", - "editButton": "Edit", - "decreaseValue": "Decrease value", - "increaseValue": "Increase value", - "decreaseValueByOne": "Decrease value by 1", - "increaseValueByOne": "Increase value by 1", - "loading": "Loading..." + "infoTitle": "Ek bilgiler", + "spinButton": "Döndür", + "editButton": "Düzenle", + "decreaseValue": "Değeri azalt", + "increaseValue": "Değeri artır", + "decreaseValueByOne": "Değeri 1 azaltın", + "increaseValueByOne": "Değeri 1 artır", + "loading": "Yükleniyor..." }, "ChartContextMenu": { - "hideData": "Hide data table", - "viewData": "View data table", - "viewInFullScreen": "View in full screen", - "printChart": "Print chart", - "downloadCSV": "Download CSV", - "downloadPNG": "Download PNG image", - "downloadJPEG": "Download JPEG image", - "downloadPDF": "Download PDF document", - "downloadSVG": "Download SVG vector image", - "downloadXLS": "Download XLS" + "hideData": "Veri tablosunu gizle", + "viewData": "Veri tablosunu görüntüle", + "viewInFullScreen": "Tam ekranda görüntüle", + "printChart": "Grafiği yazdır", + "downloadCSV": "CSV'yi indir", + "downloadPNG": "PNG resmini indir", + "downloadJPEG": "JPEG resmini indir", + "downloadPDF": "PDF belgesini indir", + "downloadSVG": "SVG vektör görüntüsünü indir", + "downloadXLS": "XLS'i indir" }, "CausalAnalysis": { "AggregateView": { @@ -39,7 +39,7 @@ "description": "Nedensel analiz, bir ürün için farklı bir fiyatlandırma stratejisi veya bir hasta için alternatif bir tedavi gibi farklı ilke seçimlerinde gerçek dünya sonuçlarının nasıl değişeceğiyle ilgili 'durum çözümlemesi' sorularını yanıtlar. Önemli bağıntı desenlerini tanımlayan model tahminlerinin aksine, bu araçlar ilgilendiğiniz sonucu doğrudan etkileyen en önemli nedensel özellikleri belirlemenize yardımcı olur. Bu modeller, diğer çelişkili özellikleri sabit tutarak bir özelliğin (genellikle “tedavi” olarak ifade edilir) nedensel etkisini tanımlar. En iyi sonuçları elde etmek için, tam veri kümesinin sonuçla ilişkili olabilecek kullanılabilir tüm özellikleri çelişkili olarak içerdiğinden emin olun.", "directAggregate": "% 95 güven aralığı ile her tedavinin doğrudan toplam nedensel etkisi", "here": "buradaki", - "infoTitle": "Additional information on aggregated causal effects", + "infoTitle": "Toplu nedensel etkiler hakkında ek bilgi", "lasso": "Bir kement (veya y ikili ise lojistik regresyon) X[-i] öğesinden y’yi ve bir kement ise (veya X[i] kategorikse lojistik regresyon) Χ[-i] öğesinden X[i] öğesini tahmin etmek için sığdırılmıştır. Nedensel etki, iki tahmin görevinin artık/sürpriz varyasyonunun ortalama bağıntısı olarak görüntülenebilir. Çift Makine Öğrenmesi hakkında daha fazla bilgi edinin", "unconfounding": "Çelişkili özellikler nelerdir?" }, @@ -51,7 +51,7 @@ "description": "Her nedensel etki, müşterilere hedeflenen bir yükseltme veya bireysel bir tedavi planı gibi kişiselleştirilmiş müdahale hakkında bilgi verebilir. Belirli özellikler kümesine sahip bir kişi nedensel bir özellikteki veya tedavideki değişikliğe nasıl yanıt verir? Nedensel durum çözümlemesi aracı, belirli bir kişinin tedavi düzeyini değiştirmeniz durumunda bu kişinin gerçek dünyada edineceği sonuçlardaki marjinal değişiklikleri hesaplar. Bu analiz, bir ürün için farklı bir fiyatlandırma stratejisi veya bir hasta için alternatif tedavi gibi farklı ilke seçimlerinde gerçek dünya sonuçlarının nasıl değiştiğini anlamanızı sağlar. İlgili tedaviyi belirtin ve gerçek dünya sonucunun nasıl değişeceğini gözlemleyin.", "directIndividual": "% 95 güven aralığı ile her tedavinin doğrudan bireysel nedensel etkisi", "index": "Datapoint dizini", - "infoTitle": "Additional information on individual causal what-if", + "infoTitle": "Bireysel nedensel etki değerlendirmesi hakkında ek bilgi", "missingParameters": "Bu sekme için değerlendirme veri kümesi sağlanması gerekir.", "newOutcome": "Yeni sonuç", "selectTreatment": "Tedavi seçin", @@ -85,7 +85,7 @@ "averageGainBinary": "{0} tedavisinin temel {1} değerine ayarlanmasından elde edilen ortalama kazanç.", "averageGainContinuous": "Hiç '{0}' işlem olmamasına kıyasla alternatif ilkelerin ortalama kazanımları.", "header": "Bu araçlar, gelecekteki müdahaleler için ilke oluşturmaya yardımcı olur. Örneğinizin hangi bölümlerinin nedensel özelliklerdeki veya tedavilerdeki değişikliklere yönelik en büyük yanıtlarla karşılaştığını belirleyebilir ve gelecekteki hangi popülasyonların belirli müdahaleler için hedeflenmesi gerektiğini tanımlayan kurallar oluşturabilirsiniz.", - "infoTitle": "Additional information on treatment policy", + "infoTitle": "Tedavi ilkesi hakkında ek bilgi", "nSample": "n = {0}", "noData": "Veri yok" } @@ -116,8 +116,8 @@ "cancel": "İptal et", "title": "Kohort Değiştir", "subText": "Kohort listesinden bir kohort seçin. Kohortu panoya uygulayın.", - "selectCohort": "Select a cohort", - "cohortList": "Cohort list" + "selectCohort": "Kohort seçin", + "cohortList": "Kohort listesi" }, "PreBuiltCohort": { "featureNameNotFound": "Özellik adı veri kümesinde bulunamadı", @@ -148,13 +148,13 @@ "predictedClass": "Tahmin edilen sınıf", "predictedValue": "Tahmin edilen değer" }, - "Size": "Size", - "loading": "Loading...", + "Size": "Boyut", + "loading": "Yükleniyor...", "counterfactualEx": "{0} karşıolgusal örneği", "counterfactualName": "Etki değerlendirmesi karşıolgusal adı", "createWhatIfCounterfactual": "Etki değerlendirmesi karşıolgusalı oluştur", "createCounterfactual": "Counterfactual", - "revertToBubbleChart": "View bubble chart", + "revertToBubbleChart": "Kabarcık grafiğini görüntüle", "createOwn": "Kendi karşıolgusalınızı oluşturun:", "currentClass": "Geçerli sınıf", "currentRange": "Geçerli aralık", @@ -167,9 +167,9 @@ "listDescription": "Bu listede, tahmini nedensel modele dahil edilen tüm özelliklere bağlı olarak geçerli veri örneğindeki hangi veri noktalarının seçili tedaviye yönelik en büyük nedensel yanıtı olduğu gösterilir. Sol beş sütunda; tedavinin gözlem için önerilip önerilmediği, geçerli tedavi, tedavinin tahmini etkisi (ikili tedavi için hiçbir tedavinin olmadığı bir temelden tedavi uygulamanın veya tedavi özelliğini örnekteki tipik tedavi boyutunun %10’u kadar artırmanın/azaltmanın etkisi: [dinamik: kullandığımız tedavideki sayısal değişikliği bildirin] ) ve bu etkinin alt ve üst güvenilirlik aralıkları (CI) bildirilir. Kalan sütunlarda, geçerli tedavi durumu ve her gözlem için diğer özellikler gösterilir.", "localImportanceDescription": "İstenen model tahminini elde etmek için {0} numaralı Satırdaki bozulacak en çok puan alan özellikler. {1} tahmini için durum çözümlemesi temel alınır", "localImportanceSelectData": "Yerel önem derecesi grafiğini görüntülemek için bir veri noktası seçin", - "largeLocalImportanceSelectData": "Select a bubble, followed by a data point to view local importance chart", - "localImportanceFetchError": "There was an error while fetching the local importance data. Error details: {0} Please check the data used.", - "BubbleChartFetchError": "There was an error while fetching the data. Error details: {0} Please check the data used.", + "largeLocalImportanceSelectData": "Yerel önem derecesi grafiğini görüntülemek için bir kabarcık, ardından bir veri noktası seçin", + "localImportanceFetchError": "Yerel önem verileri getirilirken bir hata oluştu. Hata ayrıntıları: {0} Lütfen kullanılan verileri kontrol edin.", + "BubbleChartFetchError": "Veriler getirilirken bir hata oluştu. Hata ayrıntıları: {0} Lütfen kullanılan verileri kontrol edin.", "noData": "Veri yok", "noFeatures": "Kullanılabilir özellik yok", "panelDescription": "Karşı işlemlere göz atın ve kendinizinkini oluşturun. Çeşitli karşı işlem örneklerinden önerilen değerleri görmek için özelliklerde ara yapın. Her bir karşı işlem adının altındaki \"Değer Ayarla\" metnine tıklayarak önerilen karşı işlem özellik değerlerini ayarlayın. Karşı işleminizi adlandırın ve kaydedin.", @@ -223,13 +223,13 @@ "subText": "Seçili kohort hakkında bilgi edinin. Kohort adını düzenleyin. Bu kohortu silin." }, "FeatureList": { - "featureList": "Feature List", + "featureList": "Özellik Listesi", "apply": "Uygula", "features": "Özellikler", "importances": "Önem dereceleri", "treeMapDescription": "Ağaç haritasını yeniden eğitmek için aşağıdaki özellikleri seçin ve kaydedin. Özellik önem dereceleri, doğru etiketlerinde hata bulunan iki taraflı bilgiler kullanılarak hesaplanır. Ağaç haritasını eğitirken bunu kılavuz olarak kullanın.", "staticTreeMapDescription": "Ağaç haritasını eğitmek için kullanılan özellikleri görüntüleyin. Özellik önem dereceleri, gerçek etiketlerde hatayla birlikte karşılıklı bilgiler kullanılarak hesaplandı.", - "searchResultMessage": "Results displayed out of {resultLength} for {searchValue}" + "searchResultMessage": "{searchValue} için {resultLength} içinde görüntülenen sonuçlar" }, "TreeViewParameters": { "maximumDepth": "En fazla derinlik", @@ -295,7 +295,7 @@ "disabledWarning": "Genel kohort, tam veri kümesi için oluşturulan ısı haritası nedeniyle \"Tüm verileri\" temsil edecek şekilde değiştirilmediyse hata ısı haritası devre dışı bırakılır. Hata ısı haritasını görüntülemek için tam veri kümesine geri dönün." }, "MatrixSummary": { - "heatMapInfoTitle": "Additional information on heat map", + "heatMapInfoTitle": "Isı haritası hakkında ek bilgi", "heatMapDescription": "Isı haritasıyla kesişen belirli özellik filtrelerine odaklanabilir ve bölünmüş hata oranlarını hesaplayabilirsiniz. Karşılaştırmak için iki veri kümesi özelliğiyle başlayın.", "heatMapStaticDescription": "Isı haritasıyla belirli kesişim özellik filtrelerini ve ayrılmış hata oranlarını hesaplamaya odaklanabilirsiniz. Panoyu görüntülemeden önce SDK aracılığıyla ısı haritası oluşturmak için en fazla iki özellik seçilmelidir." }, @@ -311,108 +311,108 @@ }, "Metrics": { "AccuracyScore": { - "Name": "Accuracy score", - "Info": "The accuracy score represents the ratio of correct to total instances in the data.", - "Short": "Accuracy", - "Title": "Additional information on accuracy score" + "Name": "Doğruluk puanı", + "Info": "Doğruluk puanı, verideki doğru ve toplam örneklerin oranını temsil eder.", + "Short": "Doğruluk", + "Title": "Doğruluk puanı hakkında ek bilgiler" }, "ErrorRate": { - "Name": "Error rate", - "Info": "The error rate represents the percentage of instances in the node for which the system has failed.", - "Short": "Error rate", - "Title": "Additional information on error rate" + "Name": "Hata oranı", + "Info": "Hata oranı, düğümde sistemin başarısız olduğu örneklerin yüzdesini temsil eder.", + "Short": "Hata oranı", + "Title": "Hata oranıyla ilgili ek bilgiler" }, "F1Score": { - "Name": "F1 score", - "Info": "The F1 score is the harmonic mean of the precision and recall metrics.", - "Short": "F1 score", - "Title": "Additional information on F1 score" + "Name": "F1 skoru", + "Info": "F1 skoru, duyarlık ve yakalama ölçümlerinin armonik ortalamasıdır.", + "Short": "F1 skoru", + "Title": "F1 skoru hakkında ek bilgi" }, "MeanAbsoluteError": { - "Name": "Mean absolute error", - "Info": "The mean absolute error is the average of the sum of the errors.", - "Short": "Mean abs. error", - "Title": "Additional information on mean absolute error" + "Name": "Ortalama mutlak hata sayısı", + "Info": "Ortalama mutlak hata, hataların toplamının ortalamasıdır.", + "Short": "Ortalama mutlak hata", + "Title": "Ortalama mutlak hata hakkında ek bilgi" }, "MeanSquaredError": { - "Name": "Mean squared error", - "Info": "The mean squared error is the average of the squares of the errors.", - "Short": "Mean sq. error", - "Title": "Additional information on mean squared error" + "Name": "Ortalama hata karesi", + "Info": "Ortalama hata karesi hataların karelerinin ortalamasıdır.", + "Short": "Ortalama hata karesi", + "Title": "Ortalama hata karesi hakkında ek bilgiler" }, "Precision": { - "Name": "Precision score", - "Info": "The precision is the ratio of true positives over all predicted positives.", - "Short": "Precision", - "Title": "Additional information on precision" + "Name": "Duyarlık puanı", + "Info": "Duyarlık, gerçek pozitiflerin tüm tahmin edilen pozitiflere oranıdır.", + "Short": "Duyarlık", + "Title": "Duyarlık hakkında ek bilgiler" }, "Recall": { - "Name": "Recall score", - "Info": "The recall is the ratio of true positives over all actual positives.", - "Short": "Recall", - "Title": "Additional information on recall" + "Name": "Yakalama puanı", + "Info": "Yakalama, gerçek pozitiflerin tüm pozitiflere oranıdır.", + "Short": "Yakalama", + "Title": "Yakalama hakkında ek bilgi" }, "MacroPrecision": { - "Name": "Macro averaged precision score", - "Info": "The macro averaged precision is the ratio of true positives over all predicted positives computed independently per class and averaged.", - "Short": "Macro precision", - "Title": "Additional information on macro averaged precision" + "Name": "Makro ortalama duyarlık puanı", + "Info": "Makro ortalama duyarlığı, sınıf başına bağımsız olarak hesaplanan ve ortalaması alınan tüm tahmin edilen pozitiflere göre gerçek pozitiflerin oranıdır.", + "Short": "Makro duyarlık", + "Title": "Makro ortalama duyarlığı hakkında ek bilgi" }, "MicroPrecision": { - "Name": "Micro averaged precision score", - "Info": "The micro averaged precision is the ratio of true positives over all predicted positives aggregated for all classes.", - "Short": "Micro precision", - "Title": "Additional information on micro averaged precision" + "Name": "Mikro ortalama duyarlık puanı", + "Info": "Mikro ortalama duyarlık, tüm sınıflar için toplanan tüm tahmin edilen pozitiflere göre gerçek pozitiflerin oranıdır.", + "Short": "Mikro duyarlık", + "Title": "Mikro ortalama duyarlığı hakkında ek bilgi" }, "MacroRecall": { - "Name": "Macro averaged recall score", - "Info": "The macro averaged recall is the ratio of true positives over all actual positives computed independently per class and averaged.", - "Short": "Macro recall", - "Title": "Additional information on macro averaged recall" + "Name": "Makro ortalama yakalama puanı", + "Info": "Makro ortalama yakalama, sınıf başına bağımsız olarak hesaplanan ve ortalaması alınan tüm pozitiflere göre gerçek pozitiflerin oranıdır.", + "Short": "Makro yakalama", + "Title": "Makro ortalamasını yakalama hakkında ek bilgi" }, "MicroRecall": { - "Name": "Micro averaged recall score", - "Info": "The micro averaged recall is the ratio of true positives over all actual positives aggregated for all classes.", - "Short": "Micro recall", - "Title": "Additional information on micro averaged recall" + "Name": "Mikro ortalama yakalama puanı", + "Info": "Mikro ortalama yakalama, tüm sınıflar için toplanan tüm gerçek pozitiflere göre gerçek pozitiflerin oranıdır.", + "Short": "Mikro yakalama", + "Title": "Mikro ortalamasını yakalama hakkında ek bilgiler" }, "MacroF1Score": { - "Name": "Macro averaged F1 score", - "Info": "The macro averaged F1 score is the harmonic mean of the macro averaged precision and recall metrics.", - "Short": "Macro F1 score", - "Title": "Additional information on macro averaged F1 score" + "Name": "Makro ortalama F1 skoru", + "Info": "Makro ortalama F1 skoru, makro ortalama duyarlık ve yakalama ölçümlerinin armonik ortalamasıdır.", + "Short": "Makro F1 skoru", + "Title": "Makro ortalama F1 skoru hakkında ek bilgi" }, "MicroF1Score": { - "Name": "Micro averaged F1 score", - "Info": "The micro averaged F1 score is the harmonic mean of the micro averaged precision and recall metrics.", - "Short": "Micro F1 score", - "Title": "Additional information on micro averaged F1 score" + "Name": "Mikro ortalama F1 skoru", + "Info": "Mikro ortalama F1 skoru, mikro ortalama duyarlık ve yakalama ölçümlerinin armonik ortalamasıdır.", + "Short": "Mikro F1 skoru", + "Title": "Mikro ortalama F1 skoru hakkında ek bilgi" }, "MeanAveragePrecision": { - "Name": "Mean average precision score", - "Info": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall.", - "Short": "Mean avg precision", - "Title": "Additional information on mean average precision score" + "Name": "Aritmetik ortalama duyarlık puanı", + "Info": "Nesne algılama modelleri için aritmetik ortalama duyarlık, tüm sınıflardaki AP (ortalama duyarlık) ortalamasıdır. Bu, nesne algılama modelinizin sağlamlık durumunu değerlendirir ve duyarlık ile geri çağırma arasındaki dengeyi içerir.", + "Short": "Aritmetik ort. duyarlık", + "Title": "Aritmetik ortalama duyarlık puanı hakkında ek bilgi" }, "AveragePrecision": { - "Name": "Average precision score", - "Info": "Average precision for object detection models is calculated for a selected class.", - "Short": "Avg precision", - "Title": "Additional information on average precision score" + "Name": "Ortalama duyarlık puanı", + "Info": "Nesne algılama modelleri için ortalama duyarlık seçili bir sınıf için hesaplanır.", + "Short": "Ort. duyarlık", + "Title": "Ortalama duyarlık puanı hakkında ek bilgi" }, "AverageRecall": { - "Name": "Average recall score", - "Info": "Average recall for object detection models is calculated for a selected class.", - "Short": "Avg recall", - "Title": "Additional information on average recall score" + "Name": "Ortalama geri çağırma puanı", + "Info": "Nesne algılama modelleri için ortalama geri çağırma, seçili bir sınıf için hesaplanır.", + "Short": "Ort. geri çağırma", + "Title": "Ortalama geri çağırma puanı hakkında ek bilgi" }, "metricName": "Ölçüm adı", "metricValue": "Ölçüm değeri" }, "MetricSelector": { "selectorLabel": "Ölçüm seçin", - "feature1SelectorLabel": "Rows: Feature 1", - "feature2SelectorLabel": "Columns: Feature 2" + "feature1SelectorLabel": "Satırlar: Özellik 1", + "feature2SelectorLabel": "Sütunlar: Özellik 2" }, "Navigation": { "cohortSaved": "Yeni kohort kaydedildi! Kohort ayarları altındaki Kohort listesine bakın.", @@ -433,9 +433,9 @@ "defaultLabelCopy": "Tüm veri kopyalama" }, "TreeView": { - "ariaLabel": "Interactive chart", - "disabledArialLabel": "Disabled interactive chart", - "treeMapInfoTitle": "Additional information on tree map", + "ariaLabel": "Etkileşimli grafik", + "disabledArialLabel": "Devre dışı etkileşimli grafik", + "treeMapInfoTitle": "Ağaç haritası hakkında ek bilgi", "treeDescription": "Ağaç görselleştirmesi, verilerde hiyerarşik olarak hata örneklerini başarı örneklerinden en iyi şekilde ayırmak için her bir özellik ve hata arasındaki karşılıklı bilgileri kullanır. Bu, yaygın hata desenlerini keşfetme ve vurgulama sürecini basitleştirir. Önemli hata desenlerini bulmak için, daha güçlü kırmızı renge (yani yüksek hata oranına) ve daha yüksek bir doldurma çizgisine (yani yüksek hata kapsamına) sahip düğümleri arayın. Ağaçta kullanılan özelliklerin listesini düzenlemek için \"Özellik listesi\" üzerine tıklayın. Hata ve başarı düğümlerinizin performansı hakkında daha fazla bilgi edinmek için \"metrik seç\" açılır menüsünü kullanın. Lütfen bu ölçüm seçiminin hata ağacınızın oluşturulma şeklini etkilemeyeceğini unutmayın.", "treeStaticDescription": "Ağaç görselleştirmesi, verilerde hiyerarşik olarak hata örneklerini başarı örneklerinden en iyi şekilde ayırmak için her bir özellik ve hata arasındaki karşılıklı bilgileri kullanır. Bu, yaygın hata desenlerini keşfetme ve vurgulama sürecini basitleştirir. Önemli hata desenlerini bulmak için, daha güçlü kırmızı renge (yani yüksek hata oranına) ve daha yüksek bir doldurma çizgisine (yani yüksek hata kapsamına) sahip düğümleri arayın. Bu hata ağacını oluştururken kullanılan özelliklerin listesini görüntülemek için \"Özellik listesi\" üzerine tıklayın. Hata ve başarı düğümlerinizin performansı hakkında daha fazla bilgi edinmek için \"metrik seç\" açılır menüsünü kullanın. Lütfen bu ölçüm seçiminin hata ağacınızın oluşturulma şeklini etkilemeyeceğini unutmayın.", "disabledWarning": "Genel kohort, tam veri kümesi için oluşturulan ağaç haritası nedeniyle \"Tüm verileri\" temsil edecek şekilde değiştirilmediyse hata ağaç haritası devre dışı bırakılır. Hata ağaç haritasını görüntülemek için tam veri kümesine geri dönün." @@ -770,7 +770,7 @@ "countHelperText": "Nokta sayısının histogramı", "ditherLabel": "Titreşmeli", "groupByCohort": "Kohorta göre gruplandır", - "logarithmicScaling": "Enable logarithmic scaling", + "logarithmicScaling": "Logaritmik ölçeği etkinleştir", "numOfBins": "Bölme sayısı", "selectClass": "Sınıf seçin", "selectFeature": "Özellik seçin", @@ -794,7 +794,7 @@ "importancePrefix": "Önem derecesi", "numberOfDatapoints": "Veri noktası sayısı", "rowIndex": "Satır dizini", - "absoluteIndex": "Absolute index", + "absoluteIndex": "Mutlak dizin", "xValue": "X değeri", "yValue": "Y değeri" }, @@ -822,12 +822,12 @@ }, "CohortEditor": { "columns": { - "index": "Index", - "dataset": "Dataset", - "predictedY": "Predicted Y", - "trueY": "True Y", - "classificationOutcome": "Classification outcome", - "regressionError": "Error" + "index": "Dizin", + "dataset": "Veri kümesi", + "predictedY": "Tahmin edilen Y", + "trueY": "Doğru Y", + "classificationOutcome": "Sınıflandırma sonucu", + "regressionError": "Hata" }, "TreatAsCategorical": "Kategorik olarak değerlendir", "addFilter": "Filtre ekleme", @@ -852,8 +852,8 @@ "save": "Kaydet", "saveAndSwitch": "Kaydet ve değiştir", "selectFilter": "Filtre seçin", - "noFiltersApplied": "No filters applied", - "filterAdded": "Filter added" + "noFiltersApplied": "Filtre uygulanmadı", + "filterAdded": "Filtre eklendi" }, "Columns": { "classificationOutcome": "Sınıflandırma sonucu", @@ -863,8 +863,8 @@ "falsePositive": "Hatalı pozitif", "none": "Sayı", "predictedProbabilities": "Tahmin olasılıkları", - "predictedLabels": "Predicted labels", - "trueLabels": "True labels", + "predictedLabels": "Tahmin edilen etiketler", + "trueLabels": "Doğru etiketler", "regressionError": "Regresyon hatası", "trueNegative": "Gerçek negatif", "truePositive": "Doğru pozitif", @@ -885,7 +885,7 @@ "aggregatePlots": "Kümelenmiş çizimler", "chartType": "Grafik türü", "colorValue": "Renk değeri", - "infoTitle": "Additional information on data analysis chart view", + "infoTitle": "Veri analizi grafiği görünümü hakkında ek bilgi", "helperText": "Tahmin edilen sonuç, veri kümesi özellikleri ve hata grupları gibi filtreler boyunca veri kümesi istatistiklerini analiz etmek için veri kümesi kohortları oluşturun. Veri kümenizdeki fazla/eksik sunum hakkında bilgi edinin.", "individualDatapoints": "Ayrı ayrı veri noktaları", "missingParameters": "Bu sekme için değerlendirme veri kümesi sağlanması gerekir.", @@ -906,6 +906,8 @@ "index": "Dizin", "output": "Çıkış", "predictedY": "Tahmini Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "Olasılık: {0}", "trueY": "Doğru Y", "xValue": "X değeri:", @@ -974,10 +976,10 @@ "dependencePlotHelperText": "Bu bağımlılık çizimi, bir özelliğin değerlerinin bunlara karşılık gelen özellik önem derecesi değerleriyle ilişkisini gösterir.", "dependencePlotTitle": "Bağımlılık çizimleri", "helperText": "Genel model tahminlerinizi etkileyen top-k önemli özelliklerini keşfedin (genel açıklama olarak da bilinir). Azalan özellik önem derecelerini göstermek için kaydırıcıyı kullanın. Tüm kohortların özellikleri yan yana gösterilir ve göstergede kohort seçilerek devre dışı bırakılabilir. Seçili özelliğin değerlerinin tahmini nasıl etkilediği ile ilgili aşağıdaki yoğunluk çizimini görmek için grafikteki özelliklerden herhangi birine tıklayın.", - "infoTitle": "Additional information on aggregate feature importance", + "infoTitle": "Toplu özellik önem derecesi hakkında ek bilgi", "legendHelpText": "Gösterge öğelerine tıklayarak çizimdeki kohortları açıp kapatın.", "missingParameters": "Bu sekme için yerel özellik önem derecesi parametresinin sağlanması gerekir.", - "sortByCohort": "Sort by cohort", + "sortByCohort": "Kohorta göre sırala", "sortBy": "Veri noktalarına göre sırala", "topAtoB": "Önem derecelerine göre ilk {0} özellik", "viewDependencePlotFor": "Şunun için bağımlılık çizimini görüntüle:", @@ -1020,15 +1022,15 @@ }, "Statistics": { "accuracy": "Doğruluk: {0}", - "bleuScore": "Bleu score: {0}", - "bertScore": "Bert score: {0}", - "exactMatchRatio": "Exact match ratio: {0}", - "rougeScore": "Rouge Score: {0}", + "bleuScore": "Bleu skoru: {0}", + "bertScore": "Bert skoru: {0}", + "exactMatchRatio": "Tam eşleşme oranı: {0}", + "rougeScore": "Rouge Skoru: {0}", "fnr": "Hatalı negatif oranı: {0}", "fpr": "Hatalı pozitif oranı: {0}", - "hammingScore": "Hamming score: {0}", + "hammingScore": "Hamming skoru: {0}", "meanPrediction": "Ortalama tahmin {0}", - "meteorScore": "Meteor Score: {0}", + "meteorScore": "Meteor Skoru: {0}", "mse": "Ortalama hata karesi: {0}", "precision": "Duyarlık: {0}", "rSquared": "R²: {0}", @@ -1036,10 +1038,10 @@ "selectionRate": "Seçim oranı: {0}", "mae": "Ortalama mutlak hata: {0}", "f1Score": "F1 Skoru: {0}", - "samples": "Sample size: {0}", - "meanAveragePrecision": "Mean average precision: {0}", - "averagePrecision": "Average precision: {0}", - "averageRecall": "Average recall: {0}" + "samples": "Örnek boyutu: {0}", + "meanAveragePrecision": "Aritmetik ortalama duyarlık: {0}", + "averagePrecision": "Ortalama duyarlık: {0}", + "averageRecall": "Ortalama geri çekme: {0}" }, "ValidationErrors": { "addFilters": "Filtre ekle", @@ -1147,30 +1149,30 @@ "InterpretText": { "View": { "interpretibilityDashboard": "Yorumlanabilirlik Panosu", - "importantWords": "Show most important words", + "importantWords": "En önemli sözcükleri göster", "topFeatureList": "En çok kullanılan özellikler listesinin analizi", "allButton": "TÜM ÖZELLİKLER", "negButton": "NEGATİF ÖZELLİKLER", "posButton": "POZİTİF ÖZELLİKLER", - "legendText": "Positive scalar feature importances represent the extent that the words were important towards the classification of your selected label, and negative scalar feature importances represent words that encouraged your model away from your selected label.", - "legendTextForQA": "The left text box and the bar chart display the predictions of the model. The right text box shows the feature importance associated with a selected token. Positive feature importances represent the extent that the words were important towards marking the selected token as the starting/ending position of the answer.", + "legendText": "Pozitif skaler özellik önem dereceleri, sözcüğün seçili etiketinizin sınıflandırması için önemli olduğu kapsamı temsil eder, negatif skaler özellik önem dereceleri ise modelinizi seçili etiketinizden uzaklaştıran sözcükleri temsil eder.", + "legendTextForQA": "Sol metin kutusu ve çubuk grafik modelin tahminlerini görüntüler. Doğru metin kutusu, seçili bir belirteçle ilişkili özellik önem derecesini gösterir. Pozitif özellik önem dereceleri, sözcüklerin seçili belirteci yanıtın başlangıç/bitiş konumu olarak işaretlemek için önemli olduğu kapsamı temsil eder.", "label": "Etiket", "colon": ": ", - "startingPosition": "STARTING POSITION", - "endingPosition": "ENDING POSITION", - "predictedAnswer": "Predicted answer: ", - "trueAnswer": "True answer: ", - "inputs": "Inputs", - "outputs": "Outputs", - "sliderAriaLabel": "Slider for most important words" + "startingPosition": "BAŞLANGIÇ KONUMU", + "endingPosition": "BİTİŞ KONUMU", + "predictedAnswer": "Tahmin edilen yanıt: ", + "trueAnswer": "Doğru yanıt: ", + "inputs": "Girişler", + "outputs": "Çıkışlar", + "sliderAriaLabel": "En önemli sözcükler için kaydırıcı" }, "Legend": { "featureLegend": "METİN ÖZELLİĞİ GÖSTERGESİ", "posFeatureImportance": "POZİTİF ÖZELLİK ÖNEM DERECESİ", "negFeatureImportance": "NEGATİF ÖZELLİK ÖNEM DERECESİ", - "cls": "CLS: start of the sentence", - "sep": "SEP: end of the sentence", - "selectedWord": "Selected word: " + "cls": "CLS: tümcenin başlangıcı", + "sep": "SEP: tümcenin sonu", + "selectedWord": "Seçili sözcük: " }, "BarChart": { "featureImportance": "ÖZELLİK ÖNEM DERECESİ" @@ -1178,59 +1180,59 @@ }, "InterpretVision": { "Cohort": { - "close": "Close", - "errorCohortName": "Please choose a unique cohort name.", - "errorNumSelected": "Please select at least one (1) item.", - "itemsSelectedSingular": "item selected", - "itemsSelectedPlural": "items selected", - "save": "Save cohort", - "saveAndClose": "Save and close", - "saveAndSwitch": "Save and switch", - "textField": "New cohort name", - "title": "Save new cohort" + "close": "Kapat", + "errorCohortName": "Lütfen benzersiz bir kohort adı seçin.", + "errorNumSelected": "Lütfen en az bir (1) öğe seçin.", + "itemsSelectedSingular": "öğe seçildi", + "itemsSelectedPlural": "seçilen öğeler", + "save": "Kohortu kaydet", + "saveAndClose": "Kaydet ve kapat", + "saveAndSwitch": "Kaydet ve değiştir", + "textField": "Yeni kohort adı", + "title": "Yeni kohort kaydet" }, "Dashboard": { "allData": "Tüm Veriler", - "columnOne": "Image", + "columnOne": "Resim", "columnTwo": "Endeks", "columnThree": "Doğru Y", "columnFour": "Tahmini Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "Doğru", + "columnFourOD": "Yanlış", "columnFive": "Diğer meta veriler", - "chooseObject": "Choose a detected object", - "examples": "examples", + "chooseObject": "Algılanan bir nesne seçin", + "examples": "örnekler", "filter": "Filtre", - "indexLabel": "Image ", - "labelTypeDropdown": "Select label type", - "labelVisibilityDropdown": "Select labels to display", - "legendFailure": "failure", - "legendSuccess": "success", - "loading": "Computing explanation for index", - "multiselect": "Multiselect", - "notdefined": "object scenario not defined", - "objectSelect": "Object Selection", + "indexLabel": "Resim ", + "labelTypeDropdown": "Etiket türünü seçin", + "labelVisibilityDropdown": "Görüntülenecek etiketleri seçin", + "legendFailure": "hata", + "legendSuccess": "başarılı", + "loading": "Dizin için hesaplama açıklaması", + "multiselect": "Çoklu Seçim", + "notdefined": "nesne senaryosu tanımlanmadı", + "objectSelect": "Nesne Seçimi", "pageSize": "Sayfa boyutu: ", - "panelTitle": "Selected instance", - "panelExplanation": "Explanation", - "panelInformation": "Information", - "predictedLabel": "Predicted label", - "predictedY": "Predicted: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", - "prefix": "Object: ", - "rows": "Rows: ", + "panelTitle": "Seçili örnek", + "panelExplanation": "Açıklama", + "panelInformation": "Bilgi", + "predictedLabel": "Tahmin edilen etiket", + "predictedY": "Tahmin Edilen: ", + "correctDetections": "Doğru algılamalar: ", + "incorrectDetections": "Yanlış algılamalar: ", + "prefix": "Nesne: ", + "rows": "Satırlar: ", "search": "Ara", - "selectAll": "Select all", + "selectAll": "Tümünü seç", "settings": "Ayarlar", - "showAll": "Show all", + "showAll": "Tümünü göster", "tabOptionFirst": "Görüntü gezgini görünümü", "tabOptionSecond": "Tablo görünümü", - "tabOptionThird": "Class view", + "tabOptionThird": "Sınıf görünümü", "thumbnailSize": "Küçük resim boyutu", "titleBarError": "Hata örnekleri", "titleBarSuccess": "Başarı örnekleri", - "trueY": "Ground truth: " + "trueY": "Gerçek değer: " } }, "ModelAssessment": { @@ -1239,15 +1241,15 @@ "CalloutContent": "Bazı bileşenlerin (hata ağaç görünümü, hata ısı haritası) eklenmesi, aşağıdaki bileşenlerde gördüğünüz genel kohort verilerini filtrelemenizi sağlar.", "CalloutTitle": "Bileşen ekle", "TabAddedMessage": { - "DataAnalysis": "Data analysis component added", - "FeatureImportances": "Feature importances component added", - "ErrorAnalysis": "Error analysis component added", - "Fairness": "Fairness component added", - "ModelOverview": "Model overview component added", - "CausalAnalysis": "Causal analysis component added", - "Counterfactuals": "Counterfactuals component added", - "Vision": "Vision data explorer component added", - "Forecasting": "Forecasting what-if component added" + "DataAnalysis": "Veri analizi bileşeni eklendi", + "FeatureImportances": "Özellik önem derecesi bileşeni eklendi", + "ErrorAnalysis": "Hata analizi bileşeni eklendi", + "Fairness": "Eşitlik bileşeni eklendi", + "ModelOverview": "Modele genel bakış bileşeni eklendi", + "CausalAnalysis": "Nedensel analiz bileşeni eklendi", + "Counterfactuals": "Karşıolgusallar bileşeni eklendi", + "Vision": "Görüntü veri gezgini bileşeni eklendi", + "Forecasting": "Eklenen etki değerlendirmesi bileşeni tahmin etme" } }, "CausalAnalysis": { @@ -1275,7 +1277,7 @@ }, "CohortInformation": { "ShiftCohort": "Kohort değiştir", - "SwitchTimeSeries": "Switch time series", + "SwitchTimeSeries": "Zaman serisini değiştir", "NewCohort": "Yeni kohort", "DataPoints": "Veri noktası sayısı", "DefaultCohort": " (varsayılan)", @@ -1287,7 +1289,7 @@ "CohortSettingsTitle": "Kohort ayarları" }, "ComponentNames": { - "ChartView": "Chart view", + "ChartView": "Grafik görünümü", "CausalAnalysis": "Nedensel analiz", "Counterfactuals": "Karşı olgusallar", "DataAnalysis": "Veri analizi", @@ -1296,10 +1298,10 @@ "ErrorAnalysis": "Hata analizi", "Fairness": "Eşitlik", "FeatureImportances": "Özellik önem dereceleri", - "Forecasting": "Forecasting", + "Forecasting": "Tahmin etme", "ModelOverview": "Modele genel bakış", - "TableView": "Table view", - "VisionTab": "Vision data explorer" + "TableView": "Tablo görünümü", + "VisionTab": "Görüntü veri gezgini" }, "DashboardSettings": { "Content": "Bu liste, panonun düzenini gösterir. Aşağıdaki bileşenlerde görüntülenmek üzere, hata analizi bileşenini kullanarak verileri filtreleyebilirsiniz.", @@ -1458,16 +1460,16 @@ "GlobalExplanation": "Kümelenmiş özellik önem derecesi", "IncorrectPredictions": "Yanlış tahminler", "InfoTitle": "Additional information on feature importance values", - "IndividualFeatureTabular": "Select a datapoint by clicking on a datapoint (up to 5 datapoints) in the table to view their local feature importance values (local explanation) and individual conditional expectation (ICE) plots.", + "IndividualFeatureTabular": "Yerel özellik önem değerlerini (yerel açıklama) ve bireysel koşullu beklenti (ICE) grafiğini görüntülemek için tabloda bir veri noktasına (5 veri noktasına kadar) tıklayarak bir veri noktası seçin.", "IndividualFeatureText": "Select a datapoint by clicking on a datapoint in the table to view the local feature importance values (local explanation) from SHAP's text explainer.", "LocalExplanation": "Bireysel özellik önem derecesi", "SelectionCounter": "{0}/{1} veri noktası seçildi", "SelectionLimit": "Şu anda en fazla 5 veri noktası seçilebilir.", - "RowCheckboxAriaLabel": "Row checkbox", - "SelectionColumnAriaLabel": "Toggle selection" + "RowCheckboxAriaLabel": "Satır onay kutusu", + "SelectionColumnAriaLabel": "Seçimi değiştirin" }, "IndividualFeatureImportanceView": { - "SmallInstanceSelection": "Instance selection" + "SmallInstanceSelection": "Örnek seçimi" }, "MainMenu": { "DashboardSettings": "Pano yapılandırması", @@ -1483,44 +1485,44 @@ "ModelOverview": { "metrics": { "accuracy": { - "name": "Accuracy score", + "name": "Doğruluk puanı", "description": "Doğru olarak sınıflandırılmış veri noktalarının kesir değeri." }, "exactMatchRatio": { - "name": "Exact match ratio", - "description": "The ratio of instances classified correctly for every label." + "name": "Tam eşleşme oranı", + "description": "Her etiket için doğru sınıflandırılmış örneklerin oranı." }, "meteorScore": { - "name": "Meteor Score", - "description": "METEOR Score is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision in question answering task." + "name": "Meteor Skoru", + "description": "METEOR Skoru, duyarlık ve geri çağırmanın harmonik ortalaması temel alınarak hesaplanıp soru yanıtlama görevinde geri çağırma, duyarlıktan daha fazla ağırlıklıdır." }, "bleuScore": { - "name": "Bleu Score", - "description": "Bleu Score measures the ratio of words (and/or n-grams) in the machine generated text that appeared in the reference text in question answering task." + "name": "Bleu Skoru", + "description": "Bleu Skoru, soru yanıtlama görevinde başvuru metninde görünüp makine tarafından oluşturulan metinlerdeki sözcüklerde (ve/veya n gram) oranını ölçer." }, "bertScore": { - "name": "Bert Score", - "description": "BERTScore focuses on computing semantic similarity between tokens of reference and machine generated text in question answering task." + "name": "Bert Skoru", + "description": "BERTScore, soru yanıtlama görevinde başvuru belirteçleri ile makine tarafından oluşturulan metinler arasındaki anlamsal benzerliği hesaplamaya odaklanır." }, "rougeScore": { - "name": "Rouge Score", - "description": "Rouge Score measures the ratio of words (and/or n-grams) in the reference text that appeared in the machine generated text in question answering task." + "name": "Rouge Skoru", + "description": "Rouge Skoru, soru yanıtlama görevinde başvuru metninde görünüp makine tarafından oluşturulan başvuru metinindeki sözcüklerde (ve/veya n gram) oranını ölçer." }, "hammingScore": { - "name": "Hamming score", - "description": "The average ratio of labels classified correctly among those classified as 1 in multilabel task." + "name": "Hamming skoru", + "description": "Çoklu etiket görevinde 1 olarak sınıflandırılanlar arasında doğru sınıflandırılan etiketlerin ortalama oranı." }, "f1Score": { "name": "F1 skoru", "description": "F1 skoru, duyarlık ile yakalamanın harmonik ortalamasıdır." }, "f1ScoreMacro": { - "name": "Macro F1 score", - "description": "Macro F1 score is the harmonic mean of precision and recall for each class, with each class weighted equally." + "name": "Makro F1 skoru", + "description": "Makro F1 skoru, her sınıf için duyarlık ve geri çağırmanın harmonik ortalaması olup her sınıf eşit ağırlıklıdır." }, "f1ScoreMicro": { - "name": "Micro F1 score", - "description": "Micro F1 score is the harmonic mean of precision and recall for each class, with each class weighted according to how many instances it contains." + "name": "Mikro F1 skoru", + "description": "Makro F1 skoru, her sınıf için duyarlık ve geri çağırmanın harmonik ortalaması olup her sınıf, içerdiği örnek sayısına göre eşit ağırlıklıdır." }, "meanAbsoluteError": { "name": "Ortalama mutlak hata", @@ -1535,24 +1537,24 @@ "description": "1 olarak sınıflandırılanların arasında doğru şekilde sınıflandırılmış veri noktalarının kesir değeri." }, "precisionMacro": { - "name": "Macro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted equally." + "name": "Makro Duyarlık skoru", + "description": "Her sınıfın, eşit ağırlıklandırıldığı ve her sınıf için 1 olarak sınıflandırılanlar arasında doğru bir şekilde sınıflandırılan veri noktalarının oranı." }, "precisionMicro": { - "name": "Micro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted according to how many instances it contains." + "name": "Mikro Duyarlık skoru", + "description": "Her sınıfın, içerdiği örneklerin sayısına göre ağırlıklandırıldığı ve her sınıf için 1 olarak sınıflandırılanlar arasında doğru bir şekilde sınıflandırılan veri noktalarının oranı." }, "recall": { "name": "Yakalama puanı", "description": "Gerçek etiketi 1 olanların arasında doğru bir şekilde sınıflandırılmış veri noktalarının kesir değeri. Alternatif adlar: gerçek pozitif oran, hassasiyet." }, "recallMacro": { - "name": "Macro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted equally." + "name": "Makro Geri Çağırma skoru", + "description": "Her sınıfın eşit ağırlıklandırıldığı ve her sınıf için gerçek etiketi 1 olanlar arasında doğru bir şekilde sınıflandırılan veri noktalarının oranı." }, "recallMicro": { - "name": "Micro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted according to how many instances it contains." + "name": "Mikro Geri Çağırma skoru", + "description": "Her sınıfın, içerdiği örneklerin sayısına göre ağırlıklandırıldığı ve her sınıf için gerçek etiketi 1 olanlar arasında doğru bir şekilde sınıflandırılan veri noktalarının oranı." }, "falsePositiveRate": { "name": "Hatalı pozitif oranı", @@ -1571,32 +1573,32 @@ "description": "Tüm tahminlerin ortalaması." }, "meanAveragePrecision": { - "name": "Mean Average Precision score", - "description": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall." + "name": "Aritmetik Ortalama Duyarlık skoru", + "description": "Nesne algılama modelleri için aritmetik ortalama duyarlık, tüm sınıflardaki AP (ortalama duyarlık) ortalamasıdır. Bu, nesne algılama modelinizin sağlamlık durumunu değerlendirir ve duyarlık ile geri çağırma arasındaki dengeyi içerir." }, "averagePrecision": { - "name": "Average Precision score", - "description": "Average precision for object detection models is calculated for a selected class." + "name": "Ortalama Duyarlık skoru", + "description": "Nesne algılama modelleri için ortalama duyarlık seçili bir sınıf için hesaplanır." }, "averageRecall": { - "name": "Average Recall score", - "description": "Average recall for object detection models is calculated for a selected class." + "name": "Ortalama Geri Çağırma skoru", + "description": "Nesne algılama modelleri için ortalama geri çağırma, seçili bir sınıf için hesaplanır." }, "fairnessMetricDifference": "Fark", "fairnessMetricRatio": "Oran" }, "metricsDropdown": "Ölçüm(ler)", - "metricsTypeDropdown": "Aggregate method", + "metricsTypeDropdown": "Toplama yöntemi", "metricTypes": { - "macro": "Macro", - "micro": "Micro" + "macro": "Makro", + "micro": "Mikro" }, - "classSelectionDropdown": "Select class(es)", + "classSelectionDropdown": "Sınıf seçin", "iouThresholdDropdown": { - "name": "IoU Threshold", - "description": "Intersection over Union quantifies the degree of overlap between the prediction and ground truth bounding box of a detected object in an image. For example, setting an IoU threshold of 70% means that a prediction with greater than 70% overlap with ground truth is True, thus influencing the definition of prediction correctness and calculation of other performance metrics.", + "name": "IOU Eşiği:", + "description": "Birleşim üzerindeki kesişim, görüntüde algılanan bir nesnenin tahmini ve gerçek değer sınırlayıcı kutusu arasındaki çakışma derecesini belirtir. Örneğin, IoU eşiğinin %70 olarak ayarlanması, %70'in üzerinde bir tahminle gerçek değerle örtüşmenin True olduğu anlamına gelir. Bu da tahmin doğruluğunun tanımını ve diğer performans ölçümlerini hesaplamayı etkiler.", "iconId": "iouThresholdIconId", - "title": "Learn about the IoU threshold" + "title": "IoU eşiği hakkında bilgi edinin" }, "notAvailable": "YOK", "countColumnHeader": "Örneklem büyüklüğü", @@ -1608,14 +1610,14 @@ "featuresDropdown": "Özellik(ler)", "metricChartDropdownSelectionHeader": "Ölçüm", "probabilityForClassSelectionHeader": "Sınıf olasılığı", - "targetSelectionHeader": "Target", + "targetSelectionHeader": "Hedef", "metricSelectionDropdownPlaceholder": "Kohortlarınızı karşılaştırmak için ölçümleri seçin.", - "classSelectionDropdownPlaceholder": "Select class name for class-based analysis.", + "classSelectionDropdownPlaceholder": "Sınıf tabanlı analiz için sınıf adı seçin.", "featureSelectionDropdownPlaceholder": "Özellik tabanlı analiz için kullanmak üzere özellikleri seçin.", "probabilityDistributionPivotItem": "Olasılık dağılımı", - "regressionDistributionPivotItem": "Target distribution", + "regressionDistributionPivotItem": "Hedef dağıtımı", "metricsVisualizationsPivotItem": "Ölçüm görselleştirmeleri", - "confusionMatrixPivotItem": "Confusion matrix", + "confusionMatrixPivotItem": "Karışıklık matrisi", "disaggregatedAnalysisFeatureSelectionPlaceholder": "Özellik tabanlı analiz oluşturmak için özellikleri seçin.", "tableCountTooltip": "Kohort {0}, {1} örnek içerir.", "tableMetricTooltip": "Kohort {1} üzerinde modele ait {0} şudur: {2}.", @@ -1626,36 +1628,36 @@ "metricSelectionButton": "Ölçüm seçin", "cohortSelectionButton": "Kohortları seçin", "probabilityLabelSelectionButton": "Etiket seçin", - "regressionTargetSelectionButton": "Choose target", + "regressionTargetSelectionButton": "Hedef seç", "selectAllCohortsOption": "Tümünü seç", "other": "Diğer", "BoxPlot": { "outlierProbability": "olasılık", "outlierLabel": "Aykırı değerler", "boxPlotSeriesLabel": "Kutu Grafiği", - "lowerWhisker": "Lower whisker", - "upperWhisker": "Upper whisker", - "median": "Median", - "lowerQuartile": "Lower quartile", - "upperQuartile": "Upper quartile" + "lowerWhisker": "Alt çizgi", + "upperWhisker": "Üst çizgi", + "median": "Ortanca değer", + "lowerQuartile": "Alt dörttebirlik", + "upperQuartile": "Üst dörttebirlik" }, "chartConfigApply": "Uygula", "chartConfigCancel": "İptal", "chartConfigDatasetCohortSelectionPlaceholder": "Veri kümesi kohortlarını seçin", "chartConfigFeatureBasedCohortSelectionPlaceholder": "Özellik tabanlı kohortları seçin", "confusionMatrix": { - "confusionMatrixCohortSelectionLabel": "Select dataset cohort", - "confusionMatrixClassSelectionLabel": "Select classes", - "confusionMatrixClassMinSelectionError": "Select at least {0} classes to visualize the confusion matrix.", - "confusionMatrixClassMaxSelectionError": "Select at most {0} classes to visualize the confusion matrix.", - "confusionMatrixClassSelectionDefaultPlaceholder": "Choose classes", - "confusionMatrixHeatmapTooltip": "{0} datapoints should be {1}, predicted to be {2}", - "confusionMatrixYAxisLabel": "True Class", - "confusionMatrixXAxisLabel": "Predicted Class", - "class": "Class" + "confusionMatrixCohortSelectionLabel": "Veri kümesi kohortlarını seçin", + "confusionMatrixClassSelectionLabel": "Sınıf seçin", + "confusionMatrixClassMinSelectionError": "Karışıklık matrisini görselleştirmek en az bir {0} sınıfı seçin.", + "confusionMatrixClassMaxSelectionError": "Karışıklık matrisini görselleştirmek en fazla bir {0} sınıfı seçin.", + "confusionMatrixClassSelectionDefaultPlaceholder": "Sınıfları seçin", + "confusionMatrixHeatmapTooltip": "{0} veri noktaları {1}olmalı, {2} olduğu tahmin ediliyor", + "confusionMatrixYAxisLabel": "Doğru Sınıf", + "confusionMatrixXAxisLabel": "Tahmin Edilen Sınıf", + "class": "Sınıf" }, "nA": "YOK", - "disaggregatedAnalysisBaseCohortDisclaimer": "The cohorts in the following feature-based analysis are based on the global cohort, {0}.", + "disaggregatedAnalysisBaseCohortDisclaimer": "Aşağıdaki özellik tabanlı analizdeki kohortlar genel kohorta bağlıdır, {0}.", "disaggregatedAnalysisBaseCohortWarning": "{0} kohortdan farklı olarak, {1} filtre içerir. Sonuç olarak, tüm veri kümesinin yalnızca bir alt kümesini yakalar ve içgörüler tam veri kümesine genelleştirilemeyebilir.", "probabilitySplineChartToggleLabel": "Eğri grafiği kullan", "countAxisLabel": "Sayı", @@ -1685,76 +1687,76 @@ "flyoutDescription": "Veri kümesi kohortları veya özellik kohortları görüntülemeyi seçebilirsiniz. Özellik kohortları kullanılamıyorsa öncelikle özellik kohortları görünümünde bir veya daha fazla özellik seçmeniz gerekir. Ardından özellik kohortları oluşturulur ve böylece özellik kohortlarını burada seçebilirsiniz." }, "regressionTargetOptions": { - "predictedY": "Predicted Y", - "trueY": "True Y", - "error": "Error" + "predictedY": "Tahmin edilen Y", + "trueY": "Doğru Y", + "error": "Hata" }, "topLevelDescription": "Tahmin değerlerinizin dağılımını ve model performans ölçümlerinin değerlerini inceleyerek modelinizin performansını değerlendirin. Önceden oluşturulmuş veya yeni oluşturulmuş farklı veri kümesi gruplarındaki performansının karşılaştırmalı bir analizine bakarak modelinizi incelemek için \"Veri kümesi grupları\" sekmesini kullanın. Hassas/hassas olmayan özellik alt gruplarındaki performansının karşılaştırmalı bir analizine bakarak modelinizi incelemek için \"Özellik kohortları\"nı kullanın. (ör. farklı cinsiyetler veya gelir düzeyleri arasındaki performans)", - "infoTitle": "Additional information on model overview", + "infoTitle": "Modele genel bakış hakkında ek bilgi", "visualDisplayToggleLabel": "Isı haritasını göster", "featureBasedViewDescription": "Özellik tabanlı (tek özelliğin seçili olması durumunda) ve kesişimli (iki özelliğin seçili olması durumunda) kohortlar arasında model performans çözümlemesini görmek için en fazla iki özellik seçin." }, "TableViewTab": { - "Heading": "View the dataset in a table format for all features and rows." + "Heading": "Veri kümesini tüm özellikler ve satırlar için tablo biçiminde görüntüleyin." } }, "Forecasting": { - "target": "Target", - "whatIfForecastingHeader": "What-if analysis", - "forecastHeader": "Forecast analysis", - "whatIfForecastingDescription": "What-if allows you to perturb features for your entire time series and observe how the model's forecast changes.", - "whatIfForecastingChooseTimeSeries": "To start, choose a time series from the options below.", - "forecastDescription": "Forecast analysis compares your model's forecast to the actual values of your time series. To enable what-if analysis, provide a dataset with features.", - "timeSeries": "Time series", - "selectTimeSeries": "Select a time series.", - "singleTimeSeries": "The dataset contains only a single time series '{0}' which has been selected by default.", - "trueY": "True Y", - "baselinePrediction": "Baseline prediction", - "forecastComparisonHeader": "Compare What-if Forecasts", - "forecastComparisonChartTitle": "Forecasts", - "forecastComparisonChartTimeAxisLabel": "Time", + "target": "Hedef", + "whatIfForecastingHeader": "Etki değerlendirmesi analizi", + "forecastHeader": "Tahmin analizi", + "whatIfForecastingDescription": "Etki değerlendirmesi, tüm zaman seriniz için özellikleri karıştırmanıza ve model tahmininin nasıl değiştiğini gözlemlemenize olanak sağlar.", + "whatIfForecastingChooseTimeSeries": "Başlamak için aşağıdaki seçeneklerden bir zaman serisi seçin.", + "forecastDescription": "Tahmin analizi, modelinizin tahminlerini zaman serinizin gerçek değerleriyle karşılaştırır. Etki değerlendirmesi analizini etkinleştirmek için özellikler içeren bir veri kümesi sağlayın.", + "timeSeries": "Zaman serisi", + "selectTimeSeries": "Zaman serisi seç.", + "singleTimeSeries": "Veri kümesi, varsayılan olarak seçilmiş yalnızca tek bir '{0}' zaman serisi içeriyor.", + "trueY": "Doğru Y", + "baselinePrediction": "Temel tahmin", + "forecastComparisonHeader": "Etki Değerlendirmesi Tahminlerini Karşılaştır", + "forecastComparisonChartTitle": "Tahminler", + "forecastComparisonChartTimeAxisLabel": "Zaman", "Transformations": { - "multiply": "multiply", - "divide": "divide", - "add": "add", - "subtract": "subtract", - "change": "change to" + "multiply": "çarp", + "divide": "böl", + "add": "ekle", + "subtract": "çıkar", + "change": "şununla değiştir:" }, "TransformationCreation": { - "title": "Create what-if scenario", - "nameLabel": "What-if scenario name", - "featureInstructions": "Choose a feature to perturb.", - "operationInstructions": "Choose an operation to apply to the feature.", - "operationDropdownHeader": "Operation", - "featureDropdownHeader": "Feature", - "valueSpinButtonHeader": "Value", - "scenarioNamingInstructionsPlaceholder": "Enter a unique name", - "scenarioNamingInstructions": "Enter a name for your what-if scenario.", - "scenarioNamingCollisionMessage": "This name exists already. Please enter a unique name.", - "scenarioNamingLengthMessage": "The name must be between 1 and 50 characters. The actual length is {0}.", - "scenarioNamingInvalidCharactersMessage": "The name can only contain alphanumeric characters, whitespaces, dashes, or underscores, and needs to start with an alphanumeric character.", - "valueErrorMessage": "For operation {0} please select a value other than {1}.", - "invalidCombinationErrorMessage": "This is identical to an existing what-if scenario. Please change the feature, operation, or value.", - "addTransformationButton": "Add Transformation", - "divisionAndMultiplicationBy": "by" + "title": "Etki değerlendirmesi senaryosu oluştur", + "nameLabel": "Etki değerlendirmesi senaryosu adı", + "featureInstructions": "Karıştırmak için bir özellik seçin.", + "operationInstructions": "Özelliğe uygulanacak bir işlem seçin.", + "operationDropdownHeader": "İşlem", + "featureDropdownHeader": "Özellik", + "valueSpinButtonHeader": "Değer", + "scenarioNamingInstructionsPlaceholder": "Benzersiz bir ad girin", + "scenarioNamingInstructions": "Etki değerlendirmesi senaryonuz için bir ad girin.", + "scenarioNamingCollisionMessage": "Bu ad zaten var. Lütfen benzersiz bir ad girin.", + "scenarioNamingLengthMessage": "Ad 1 ile 50 karakter arasında olmalıdır. Gerçek uzunluk {0}.", + "scenarioNamingInvalidCharactersMessage": "Ad yalnızca alfasayısal karakterler, boşluklar, kısa çizgiler veya alt çizgi içerebilir ve alfasayısal bir karakterle başlamalıdır.", + "valueErrorMessage": "{0} işlemi için lütfen {1} haricinden bir değer seçin.", + "invalidCombinationErrorMessage": "Bu, mevcut bir etki değerlendirmesi senaryosuyla aynı. Lütfen özelliği, işlemi veya değeri değiştirin.", + "addTransformationButton": "Dönüşüm Ekle", + "divisionAndMultiplicationBy": "gerçekleştiren" }, "TransformationTable": { - "nameColumnHeader": "Name", - "methodColumnHeader": "Method", - "divisionAndMultiplicationBy": "by ", - "header": "What-if Forecasts ({0})" + "nameColumnHeader": "Ad", + "methodColumnHeader": "Yöntem", + "divisionAndMultiplicationBy": "gerçekleştiren ", + "header": "Etki Değerlendirmesi Tahminleri ({0})" }, "TimeSeries": { - "apply": "Apply", - "cancel": "Cancel", - "cohortList": "Time series list", - "selectCohort": "Select a time series", - "shiftCohort": "Switch time series", - "shiftCohortDescription": "Select a time series from the time series list. Apply the time series to the dashboard." + "apply": "Uygula", + "cancel": "İptal", + "cohortList": "Zaman serisi listesi", + "selectCohort": "Zaman serisi seç", + "shiftCohort": "Zaman serisini değiştir", + "shiftCohortDescription": "Zaman serisi listesinden bir zaman serisi seçin. Zaman serisini panoya uygulayın." }, "TimeSeriesSettings": { - "CohortSettingsDescription": "Time series are pre-defined based on time series identifying columns.", - "CohortSettingsTitle": "Time series settings" + "CohortSettingsDescription": "Zaman serisi, sütunları tanımlayan zaman serisine göre önceden tanımlanmıştır.", + "CohortSettingsTitle": "Zaman serisi ayarları" } } } \ No newline at end of file diff --git a/libs/localization/src/lib/en.zh-Hans.json b/libs/localization/src/lib/en.zh-Hans.json index 3bd259f4f0..0a4e4acc43 100644 --- a/libs/localization/src/lib/en.zh-Hans.json +++ b/libs/localization/src/lib/en.zh-Hans.json @@ -3,26 +3,26 @@ "close": "关闭", "tooltipButton": "工具提示按钮", "identityFeature": "标识特征", - "infoTitle": "Additional information", - "spinButton": "Spin", - "editButton": "Edit", - "decreaseValue": "Decrease value", - "increaseValue": "Increase value", - "decreaseValueByOne": "Decrease value by 1", - "increaseValueByOne": "Increase value by 1", - "loading": "Loading..." + "infoTitle": "其他信息", + "spinButton": "旋转", + "editButton": "编辑", + "decreaseValue": "将值减少", + "increaseValue": "增加价值", + "decreaseValueByOne": "将值减少 1", + "increaseValueByOne": "将值增加 1", + "loading": "正在加载..." }, "ChartContextMenu": { - "hideData": "Hide data table", - "viewData": "View data table", - "viewInFullScreen": "View in full screen", - "printChart": "Print chart", - "downloadCSV": "Download CSV", - "downloadPNG": "Download PNG image", - "downloadJPEG": "Download JPEG image", - "downloadPDF": "Download PDF document", - "downloadSVG": "Download SVG vector image", - "downloadXLS": "Download XLS" + "hideData": "隐藏数据表", + "viewData": "查看数据表", + "viewInFullScreen": "全屏查看", + "printChart": "打印图表", + "downloadCSV": "下载 CSV", + "downloadPNG": "下载 PNG 图像", + "downloadJPEG": "下载 JPEG 图像", + "downloadPDF": "下载 PDF 文档", + "downloadSVG": "下载 SVG 矢量映像", + "downloadXLS": "下载 XLS" }, "CausalAnalysis": { "AggregateView": { @@ -39,7 +39,7 @@ "description": "因果分析回答“假设”问题，即在不同的策略选择下，现实世界结果会如何变化，例如产品的不同定价策略或患者的替代治疗方法。与识别重要关联模式的模型预测不同，这些工具可帮助你识别直接影响所关注结果的最重要的因果关系特征。这些模型在保持其他混杂特征不变的情况下可确定一个特征(通常称为“治疗”)的因果效应。为了获得最佳结果，请确保完整数据集包含所有可能作为混杂因素而与结果关联的可用特征。", "directAggregate": "每种处理的直接聚合因果效应(置信区间为 95%)", "here": "此处", - "infoTitle": "Additional information on aggregated causal effects", + "infoTitle": "有关聚合因果效应的其他信息", "lasso": "套索(或逻辑回归，如果 y 是二元的)适合从 X[-i] 预测 y，套索(或逻辑回归，如果 X[i] 是分类的)适合从 X[-i] 预测 X[i]。可将因果效应视为两个预测任务的残差/意外变体的平均相关性。了解有关双重机器学习的详细信息", "unconfounding": "什么是混杂特征?" }, @@ -51,7 +51,7 @@ "description": "个体因果效应可以为个性化干预提供信息，例如针对客户的针对性促销或个性化治疗计划。具有一组特定特征的个人如何响应因果特征或治疗中的变化? 如果更改特定个体的治疗级别，则因果 what-if 工具将计算特定个体在现实世界中的边际更改。通过此分析，可以了解在不同的策略选择(例如产品的不同定价策略或患者的替代治疗方法)下现实世界结果的更改情况。请指定感兴趣的治疗方法，并观察现实世界结果将如何变化。", "directIndividual": "每种处理的直接个体因果效应(置信区间为 95%)", "index": "数据点索引", - "infoTitle": "Additional information on individual causal what-if", + "infoTitle": "有关单个因果关系模拟的其他信息", "missingParameters": "此选项卡要求提供评估数据集。", "newOutcome": "新结果", "selectTreatment": "选择处理", @@ -85,7 +85,7 @@ "averageGainBinary": "将治疗 {0} 设置为其基线值 {1} 的平均增益。", "averageGainContinuous": "替代策略相对于无“{0}”治疗的平均增益。", "header": "这些工具有助于构建策略以供进行干预。你可以确定示例的哪些部分对因果特征或治疗的变化做出最大响应，并构造规则来定义特定干预未来应针对哪些人群。", - "infoTitle": "Additional information on treatment policy", + "infoTitle": "有关治疗策略的其他信息", "nSample": "n = {0}", "noData": "无数据" } @@ -116,8 +116,8 @@ "cancel": "取消", "title": "切换队列", "subText": "从队列列表中选择队列。将该队列应用到仪表板。", - "selectCohort": "Select a cohort", - "cohortList": "Cohort list" + "selectCohort": "选择队列", + "cohortList": "队列列表" }, "PreBuiltCohort": { "featureNameNotFound": "在数据集中找不到功能名称", @@ -148,13 +148,13 @@ "predictedClass": "预测类", "predictedValue": "预测值" }, - "Size": "Size", - "loading": "Loading...", + "Size": "大小", + "loading": "正在加载...", "counterfactualEx": "反事实示例 {0}", "counterfactualName": "模拟反事实名", "createWhatIfCounterfactual": "创建 What-if 反事实", "createCounterfactual": "反事实", - "revertToBubbleChart": "View bubble chart", + "revertToBubbleChart": "查看气泡图", "createOwn": "创建自己的反事实:", "currentClass": "当前类", "currentRange": "当前范围", @@ -167,9 +167,9 @@ "listDescription": "此列表根据估计因果模型中包含的所有特征，显示当前数据示例中的哪些数据点对所选治疗具有最大的因果响应。左五列报告是否建议治疗用于观察、当前治疗、估计的治疗效果(对二元治疗从无治疗的基线应用治疗的效果，或将治疗特征增加/减少示例中典型治疗大小的 10% 的效果: [动态: 报告所用治疗中的数值变化])，以及此效果的下部和上部置信区间(CI)。其余列显示当前治疗状态和每次观察的其他特征。", "localImportanceDescription": "要扰乱以实现所需模型预测的行 {0} 中排在前列的特征。基于对预测 {1} 的模拟分析", "localImportanceSelectData": "选择数据点以查看本地重要性图表", - "largeLocalImportanceSelectData": "Select a bubble, followed by a data point to view local importance chart", - "localImportanceFetchError": "There was an error while fetching the local importance data. Error details: {0} Please check the data used.", - "BubbleChartFetchError": "There was an error while fetching the data. Error details: {0} Please check the data used.", + "largeLocalImportanceSelectData": "选择气泡，后跟一个数据点以查看本地重要性图表", + "localImportanceFetchError": "提取本地重要性数据时出错。错误详细信息: {0} 请检查使用的数据。", + "BubbleChartFetchError": "提取数据时出错。错误详细信息: {0} 请检查使用的数据。", "noData": "无数据", "noFeatures": "无可用特征", "panelDescription": "浏览并创建自己的反事实。搜索特征以查看一组不同的反事实示例中的建议值。通过单击每个反事实名称下的“设置值”文本，设置建议的反事实特征值。为反事实命名并保存它。", @@ -223,13 +223,13 @@ "subText": "了解选中的队列。编辑其队列名。删除此队列。" }, "FeatureList": { - "featureList": "Feature List", + "featureList": "功能列表", "apply": "应用", "features": "特征", "importances": "重要性", "treeMapDescription": "若要重新训练树状图，请选择并保存下面的特征。特征重要性使用共同信息计算得出(错误标注在真实标签上)。 请将其用做训练树状图的指南。", "staticTreeMapDescription": "查看用于训练树状图的功能。使用相互信息计算特征重要性，并在真实标签上显示错误。", - "searchResultMessage": "Results displayed out of {resultLength} for {searchValue}" + "searchResultMessage": "为 {searchValue} 显示 {resultLength} 之外的结果" }, "TreeViewParameters": { "maximumDepth": "最大深度", @@ -295,7 +295,7 @@ "disabledWarning": "除非将全局队列切换为表示“全部数据”，否则禁用错误热度地图，因为正在为完整数据集生成热度地图。切换回完整数据集以查看错误热度地图。" }, "MatrixSummary": { - "heatMapInfoTitle": "Additional information on heat map", + "heatMapInfoTitle": "热度地图上的其他信息", "heatMapDescription": "借助热度地图，可以专注于特定的相交特征筛选器和计算非聚合错误率。请从要比较的两个数据集特征开始。", "heatMapStaticDescription": "借助热度地图，可以专注于特定的交集功能筛选器和计算非聚合错误率。在查看仪表板之前，必需最多选择两个功能才能通过 SDK 创建热度地图。" }, @@ -311,108 +311,108 @@ }, "Metrics": { "AccuracyScore": { - "Name": "Accuracy score", - "Info": "The accuracy score represents the ratio of correct to total instances in the data.", - "Short": "Accuracy", - "Title": "Additional information on accuracy score" + "Name": "准确度分数", + "Info": "准确度分数表示数据中正确实例数与实例总数的比率。", + "Short": "准确度", + "Title": "有关准确度分数的其他信息" }, "ErrorRate": { - "Name": "Error rate", - "Info": "The error rate represents the percentage of instances in the node for which the system has failed.", - "Short": "Error rate", - "Title": "Additional information on error rate" + "Name": "错误率", + "Info": "错误率表示系统失败节点中实例的百分比。", + "Short": "错误率", + "Title": "有关错误率的其他信息" }, "F1Score": { - "Name": "F1 score", - "Info": "The F1 score is the harmonic mean of the precision and recall metrics.", - "Short": "F1 score", - "Title": "Additional information on F1 score" + "Name": "F1 分数", + "Info": "F1 分数是精准率和召回率指标的调和平均值。", + "Short": "F1 分数", + "Title": "有关 F1 分数的其他信息" }, "MeanAbsoluteError": { - "Name": "Mean absolute error", - "Info": "The mean absolute error is the average of the sum of the errors.", - "Short": "Mean abs. error", - "Title": "Additional information on mean absolute error" + "Name": "平均绝对误差", + "Info": "平均绝对误差是误差总和的平均值。", + "Short": "平均 abs. 误差", + "Title": "有关平均绝对误差的其他信息" }, "MeanSquaredError": { - "Name": "Mean squared error", - "Info": "The mean squared error is the average of the squares of the errors.", - "Short": "Mean sq. error", - "Title": "Additional information on mean squared error" + "Name": "均方误差", + "Info": "均方误差是错误平方的平均值。", + "Short": "平均 sq. 误差", + "Title": "有关均方误差的其他信息" }, "Precision": { - "Name": "Precision score", - "Info": "The precision is the ratio of true positives over all predicted positives.", - "Short": "Precision", - "Title": "Additional information on precision" + "Name": "精准率分数", + "Info": "精准率是真阳性与所有预测阳性的比率。", + "Short": "精准率", + "Title": "有关精准率的其他信息" }, "Recall": { - "Name": "Recall score", - "Info": "The recall is the ratio of true positives over all actual positives.", - "Short": "Recall", - "Title": "Additional information on recall" + "Name": "召回率分数", + "Info": "召回率是指真阳性与所有实际阳性之比。", + "Short": "召回率", + "Title": "有关召回率的其他信息" }, "MacroPrecision": { - "Name": "Macro averaged precision score", - "Info": "The macro averaged precision is the ratio of true positives over all predicted positives computed independently per class and averaged.", - "Short": "Macro precision", - "Title": "Additional information on macro averaged precision" + "Name": "宏平均精准率分数", + "Info": "宏平均精准率是指真阳性与按每个类和平均独立计算的所有预测阳性之间的比率。", + "Short": "宏精准率", + "Title": "有关宏平均精准率的其他信息" }, "MicroPrecision": { - "Name": "Micro averaged precision score", - "Info": "The micro averaged precision is the ratio of true positives over all predicted positives aggregated for all classes.", - "Short": "Micro precision", - "Title": "Additional information on micro averaged precision" + "Name": "微平均精准率分数", + "Info": "微平均精准率是指真阳性与所有类聚合的所有预测阳性之比。", + "Short": "微精准率", + "Title": "有关微平均精准率的其他信息" }, "MacroRecall": { - "Name": "Macro averaged recall score", - "Info": "The macro averaged recall is the ratio of true positives over all actual positives computed independently per class and averaged.", - "Short": "Macro recall", - "Title": "Additional information on macro averaged recall" + "Name": "宏平均召回率分数", + "Info": "宏平均召回率是指真阳性与按每个类和平均独立计算的所有实际阳性之间的比率。", + "Short": "宏召回率", + "Title": "有关宏平均召回率的其他信息" }, "MicroRecall": { - "Name": "Micro averaged recall score", - "Info": "The micro averaged recall is the ratio of true positives over all actual positives aggregated for all classes.", - "Short": "Micro recall", - "Title": "Additional information on micro averaged recall" + "Name": "微平均召回率分数", + "Info": "微平均召回率是指真阳性与所有类聚合的所有实际阳性之比。", + "Short": "微召回率", + "Title": "有关微平均召回率的其他信息" }, "MacroF1Score": { - "Name": "Macro averaged F1 score", - "Info": "The macro averaged F1 score is the harmonic mean of the macro averaged precision and recall metrics.", - "Short": "Macro F1 score", - "Title": "Additional information on macro averaged F1 score" + "Name": "宏平均 F1 分数", + "Info": "宏平均 F1 分数是宏平均精准率和召回率指标的调和平均值。", + "Short": "宏 F1 分数", + "Title": "有关宏平均 F1 分数的其他信息" }, "MicroF1Score": { - "Name": "Micro averaged F1 score", - "Info": "The micro averaged F1 score is the harmonic mean of the micro averaged precision and recall metrics.", - "Short": "Micro F1 score", - "Title": "Additional information on micro averaged F1 score" + "Name": "微平均 F1 分数", + "Info": "微平均 F1 分数是微平均精度和召回率指标的调和平均值。", + "Short": "微 F1 分数", + "Title": "有关微平均召回率 F1 分数的其他信息" }, "MeanAveragePrecision": { - "Name": "Mean average precision score", - "Info": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall.", - "Short": "Mean avg precision", - "Title": "Additional information on mean average precision score" + "Name": "平均精度分数", + "Info": "对象检测模型的平均精度是所有类中 AP 的平均精度(平均精度)。这将评估对象检测模型的可靠性，并封装精度和召回率之间的权衡。", + "Short": "平均精度平均值", + "Title": "有关平均精确度平均值的其他信息" }, "AveragePrecision": { - "Name": "Average precision score", - "Info": "Average precision for object detection models is calculated for a selected class.", - "Short": "Avg precision", - "Title": "Additional information on average precision score" + "Name": "平均精度分数", + "Info": "为所选类计算对象检测模型的平均精度。", + "Short": "平均精度", + "Title": "有关平均精度分数的其他信息" }, "AverageRecall": { - "Name": "Average recall score", - "Info": "Average recall for object detection models is calculated for a selected class.", - "Short": "Avg recall", - "Title": "Additional information on average recall score" + "Name": "平均召回率分数", + "Info": "为所选类计算对象检测模型的平均召回率。", + "Short": "平均召回率", + "Title": "有关平均召回率分数的其他信息" }, "metricName": "指标名", "metricValue": "指标值" }, "MetricSelector": { "selectorLabel": "选择指标", - "feature1SelectorLabel": "Rows: Feature 1", - "feature2SelectorLabel": "Columns: Feature 2" + "feature1SelectorLabel": "行: 功能 1", + "feature2SelectorLabel": "列: 功能 2" }, "Navigation": { "cohortSaved": "已保存新队列! 请参阅队列设置下的队列列表。", @@ -433,9 +433,9 @@ "defaultLabelCopy": "所有数据复制" }, "TreeView": { - "ariaLabel": "Interactive chart", - "disabledArialLabel": "Disabled interactive chart", - "treeMapInfoTitle": "Additional information on tree map", + "ariaLabel": "交互式图表", + "disabledArialLabel": "禁用的交互式图表", + "treeMapInfoTitle": "有关树状图的其他信息", "treeDescription": "树可视化效果使用每个功能和错误之间的相互信息，以最好地将错误实例与数据中的成功实例进行分层分离。这简化了发现和突出显示常见失败模式的过程。要查找重要的失败模式，请查找红色更深的节点(即高错误率)和较高的填充线(即高错误覆盖率)。要编辑树中正在使用的功能列表，请单击“功能列表”。使用“选择指标”下拉菜单以详细了解错误和成功节点的性能。请注意，此指标选择将不会影响错误树的生成方式。", "treeStaticDescription": "树可视化效果使用每个功能和错误之间的相互信息，以最好地将错误实例与数据中的成功实例进行分层分离。这简化了发现和突出显示常见失败模式的过程。要查找重要的失败模式，请查找红色更深的节点(即高错误率)和较高的填充线(即高错误覆盖率)。要查看创建此错误树时使用的功能列表，请单击“功能列表”。使用“选择指标”下拉菜单以详细了解错误和成功节点的性能。请注意，此指标选择将不会影响错误树的生成方式。", "disabledWarning": "除非全局队列切换为表示“所有数据”，否则将禁用错误树状图，因为正在为完整数据集生成树状图。切换回完整数据集以查看错误树状图。" @@ -770,7 +770,7 @@ "countHelperText": "点数直方图", "ditherLabel": "应抖动", "groupByCohort": "按队列分组", - "logarithmicScaling": "Enable logarithmic scaling", + "logarithmicScaling": "启用对数缩放", "numOfBins": "箱数", "selectClass": "选择类", "selectFeature": "选择特征", @@ -794,7 +794,7 @@ "importancePrefix": "重要性", "numberOfDatapoints": "数据点数量", "rowIndex": "行索引", - "absoluteIndex": "Absolute index", + "absoluteIndex": "绝对索引", "xValue": "X 值", "yValue": "Y 值" }, @@ -822,12 +822,12 @@ }, "CohortEditor": { "columns": { - "index": "Index", - "dataset": "Dataset", - "predictedY": "Predicted Y", - "trueY": "True Y", - "classificationOutcome": "Classification outcome", - "regressionError": "Error" + "index": "索引", + "dataset": "数据集", + "predictedY": "预测的 Y", + "trueY": "真实 Y", + "classificationOutcome": "分类结果", + "regressionError": "错误" }, "TreatAsCategorical": "视为类别", "addFilter": "添加筛选器", @@ -852,8 +852,8 @@ "save": "保存", "saveAndSwitch": "保存并切换", "selectFilter": "选择筛选器", - "noFiltersApplied": "No filters applied", - "filterAdded": "Filter added" + "noFiltersApplied": "未应用任何筛选器", + "filterAdded": "已添加筛选器" }, "Columns": { "classificationOutcome": "分类结果", @@ -863,8 +863,8 @@ "falsePositive": "假正", "none": "计数", "predictedProbabilities": "预测概率", - "predictedLabels": "Predicted labels", - "trueLabels": "True labels", + "predictedLabels": "预测的标签", + "trueLabels": "True 标签", "regressionError": "回归错误", "trueNegative": "真负", "truePositive": "真正", @@ -885,7 +885,7 @@ "aggregatePlots": "聚合绘图", "chartType": "图表类型", "colorValue": "颜色值", - "infoTitle": "Additional information on data analysis chart view", + "infoTitle": "有关数据分析图表视图的其他信息", "helperText": "创建数据集队列，以按筛选器分析数据统计信息，如预测结果、数据集特征和错误组。了解数据集中的呈现过度/不足。", "individualDatapoints": "单个数据点", "missingParameters": "此选项卡要求提供评估数据集。", @@ -906,6 +906,8 @@ "index": "索引", "output": "输出", "predictedY": "预测的 Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "概率: {0}", "trueY": "真实 Y", "xValue": "X 值:", @@ -974,10 +976,10 @@ "dependencePlotHelperText": "此相关性图显示特征值与其相应特征重要性值之间的关系。", "dependencePlotTitle": "相关性绘图", "helperText": "探索影响总体模型预测的前 k 个重要特征(即全局解释)。使用滑块降序显示特征重要性。并排显示所有队列的功能重要性，并且可以通过选择图例中的队列来切换关闭。单击关系图中的任何特征以查看下面的密度图，以了解所选特征的值如何影响预测。", - "infoTitle": "Additional information on aggregate feature importance", + "infoTitle": "有关聚合特征重要性的其他信息", "legendHelpText": "通过单击图例项在绘图中切换启用/禁用队列。", "missingParameters": "此选项卡要求提供局部特征重要性参数。", - "sortByCohort": "Sort by cohort", + "sortByCohort": "按队列排序", "sortBy": "按数据点排序", "topAtoB": "前 {0} 个特征(按重要性排列)", "viewDependencePlotFor": "查看以下特征的相关性绘图:", @@ -1020,15 +1022,15 @@ }, "Statistics": { "accuracy": "准确度: {0}", - "bleuScore": "Bleu score: {0}", - "bertScore": "Bert score: {0}", - "exactMatchRatio": "Exact match ratio: {0}", - "rougeScore": "Rouge Score: {0}", + "bleuScore": "Bleu 评分: {0}", + "bertScore": "Bert 评分: {0}", + "exactMatchRatio": "完全匹配比率: {0}", + "rougeScore": "Rouge 评分: {0}", "fnr": "误报率: {0}", "fpr": "漏报率: {0}", - "hammingScore": "Hamming score: {0}", + "hammingScore": "汉明评分: {0}", "meanPrediction": "平均值预测 {0}", - "meteorScore": "Meteor Score: {0}", + "meteorScore": "流星评分: {0}", "mse": "均方误差: {0}", "precision": "精准率: {0}", "rSquared": "R²: {0}", @@ -1036,10 +1038,10 @@ "selectionRate": "选择率: {0}", "mae": "平均绝对误差: {0}", "f1Score": "F1 分数: {0}", - "samples": "Sample size: {0}", - "meanAveragePrecision": "Mean average precision: {0}", - "averagePrecision": "Average precision: {0}", - "averageRecall": "Average recall: {0}" + "samples": "样本大小: {0}", + "meanAveragePrecision": "平均精度均值: {0}", + "averagePrecision": "平均精度: {0}", + "averageRecall": "平均召回率: {0}" }, "ValidationErrors": { "addFilters": "添加筛选器", @@ -1147,30 +1149,30 @@ "InterpretText": { "View": { "interpretibilityDashboard": "可解释性仪表板", - "importantWords": "Show most important words", + "importantWords": "显示最重要的字词", "topFeatureList": "主要特征列表分析", "allButton": "所有特征", "negButton": "负特征", "posButton": "正特征", - "legendText": "Positive scalar feature importances represent the extent that the words were important towards the classification of your selected label, and negative scalar feature importances represent words that encouraged your model away from your selected label.", - "legendTextForQA": "The left text box and the bar chart display the predictions of the model. The right text box shows the feature importance associated with a selected token. Positive feature importances represent the extent that the words were important towards marking the selected token as the starting/ending position of the answer.", + "legendText": "正标量特征重要性表示该词对你所选标签的分类的重要程度，而负标量特征重要性表示驱使你的模型远离你所选标签的词。", + "legendTextForQA": "左侧文本框和条形图显示模型的预测。右侧文本框显示与所选令牌关联的功能重要性。正面特征重要性表示字词对于将所选令牌标记为答案的开始/结束位置非常重要的程度。", "label": "标签", "colon": ":", - "startingPosition": "STARTING POSITION", - "endingPosition": "ENDING POSITION", - "predictedAnswer": "Predicted answer: ", - "trueAnswer": "True answer: ", - "inputs": "Inputs", - "outputs": "Outputs", - "sliderAriaLabel": "Slider for most important words" + "startingPosition": "起始位置", + "endingPosition": "结束位置", + "predictedAnswer": "预测的答案:", + "trueAnswer": "真实答案:", + "inputs": "输入", + "outputs": "输出", + "sliderAriaLabel": "用于最重要的字词的滑块" }, "Legend": { "featureLegend": "文本特征图例", "posFeatureImportance": "正特征重要性", "negFeatureImportance": "负特征重要性", - "cls": "CLS: start of the sentence", - "sep": "SEP: end of the sentence", - "selectedWord": "Selected word: " + "cls": "CLS: 句子的开头", + "sep": "SEP: 句子结尾", + "selectedWord": "所选字词:" }, "BarChart": { "featureImportance": "特征重要性" @@ -1178,59 +1180,59 @@ }, "InterpretVision": { "Cohort": { - "close": "Close", - "errorCohortName": "Please choose a unique cohort name.", - "errorNumSelected": "Please select at least one (1) item.", - "itemsSelectedSingular": "item selected", - "itemsSelectedPlural": "items selected", - "save": "Save cohort", - "saveAndClose": "Save and close", - "saveAndSwitch": "Save and switch", - "textField": "New cohort name", - "title": "Save new cohort" + "close": "关闭", + "errorCohortName": "请选择唯一的队列名称。", + "errorNumSelected": "请选择至少 (1) 项。", + "itemsSelectedSingular": "已选择项", + "itemsSelectedPlural": "选定项目", + "save": "保存对列", + "saveAndClose": "保存并关闭", + "saveAndSwitch": "保存并切换", + "textField": "新队列名称", + "title": "保存新队列" }, "Dashboard": { "allData": "所有数据", - "columnOne": "Image", + "columnOne": "图像", "columnTwo": "指标", "columnThree": "真实 Y", "columnFour": "预测的 Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "正确", + "columnFourOD": "不正确", "columnFive": "其他元数据", - "chooseObject": "Choose a detected object", - "examples": "examples", + "chooseObject": "选择检测到的对象", + "examples": "示例", "filter": "筛选器", - "indexLabel": "Image ", - "labelTypeDropdown": "Select label type", - "labelVisibilityDropdown": "Select labels to display", - "legendFailure": "failure", - "legendSuccess": "success", - "loading": "Computing explanation for index", - "multiselect": "Multiselect", - "notdefined": "object scenario not defined", - "objectSelect": "Object Selection", + "indexLabel": "图像 ", + "labelTypeDropdown": "选择标签类型", + "labelVisibilityDropdown": "选择要显示的标签", + "legendFailure": "故障", + "legendSuccess": "成功", + "loading": "索引的计算说明", + "multiselect": "多选", + "notdefined": "未定义对象方案", + "objectSelect": "对象选择", "pageSize": "页面大小:", - "panelTitle": "Selected instance", - "panelExplanation": "Explanation", - "panelInformation": "Information", - "predictedLabel": "Predicted label", - "predictedY": "Predicted: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", - "prefix": "Object: ", - "rows": "Rows: ", + "panelTitle": "所选实例", + "panelExplanation": "说明", + "panelInformation": "信息", + "predictedLabel": "预测标签", + "predictedY": "预测:", + "correctDetections": "检测正确:", + "incorrectDetections": "检测不正确:", + "prefix": "对象: ", + "rows": "行数: ", "search": "搜索", - "selectAll": "Select all", + "selectAll": "全选", "settings": "设置", - "showAll": "Show all", + "showAll": "全部显示", "tabOptionFirst": "图像资源管理器视图", "tabOptionSecond": "表视图", - "tabOptionThird": "Class view", + "tabOptionThird": "类视图", "thumbnailSize": "缩略图大小", "titleBarError": "错误实例", "titleBarSuccess": "成功实例", - "trueY": "Ground truth: " + "trueY": "基本事实:" } }, "ModelAssessment": { @@ -1239,15 +1241,15 @@ "CalloutContent": "通过添加一些组件(错误树状视图、错误热度地图)，可以从在以下组件内看到的全局队列中筛选数据。", "CalloutTitle": "添加组件", "TabAddedMessage": { - "DataAnalysis": "Data analysis component added", - "FeatureImportances": "Feature importances component added", - "ErrorAnalysis": "Error analysis component added", - "Fairness": "Fairness component added", - "ModelOverview": "Model overview component added", - "CausalAnalysis": "Causal analysis component added", - "Counterfactuals": "Counterfactuals component added", - "Vision": "Vision data explorer component added", - "Forecasting": "Forecasting what-if component added" + "DataAnalysis": "添加了数据分析组件", + "FeatureImportances": "添加了特征重要性组件", + "ErrorAnalysis": "添加了错误分析组件", + "Fairness": "添加了公平性组件", + "ModelOverview": "已添加模型概述组件", + "CausalAnalysis": "添加了因果分析组件", + "Counterfactuals": "添加了反事实组件", + "Vision": "已添加视觉数据资源管理器组件", + "Forecasting": "预测添加模拟组件" } }, "CausalAnalysis": { @@ -1275,7 +1277,7 @@ }, "CohortInformation": { "ShiftCohort": "切换队列", - "SwitchTimeSeries": "Switch time series", + "SwitchTimeSeries": "切换时序", "NewCohort": "新建队列", "DataPoints": "数据点数量", "DefaultCohort": " (默认)", @@ -1287,7 +1289,7 @@ "CohortSettingsTitle": "队列设置" }, "ComponentNames": { - "ChartView": "Chart view", + "ChartView": "图表视图", "CausalAnalysis": "因果分析", "Counterfactuals": "反事实", "DataAnalysis": "数据分析", @@ -1296,10 +1298,10 @@ "ErrorAnalysis": "错误分析", "Fairness": "公平度", "FeatureImportances": "特征重要性", - "Forecasting": "Forecasting", + "Forecasting": "预测", "ModelOverview": "模型概述", - "TableView": "Table view", - "VisionTab": "Vision data explorer" + "TableView": "表视图", + "VisionTab": "视觉数据资源管理器" }, "DashboardSettings": { "Content": "此表显示了仪表板的布局。可以使用错误分析组件向下筛选要在以下组件中查看的数据。", @@ -1458,16 +1460,16 @@ "GlobalExplanation": "聚合特征重要性", "IncorrectPredictions": "不正确的预测", "InfoTitle": "Additional information on feature importance values", - "IndividualFeatureTabular": "Select a datapoint by clicking on a datapoint (up to 5 datapoints) in the table to view their local feature importance values (local explanation) and individual conditional expectation (ICE) plots.", + "IndividualFeatureTabular": "通过单击表中的一个数据点(最多 5 个数据点)来选择数据点，以查看其本地特征重要性值(本地解释)和单个条件预期(ICE)绘图。", "IndividualFeatureText": "Select a datapoint by clicking on a datapoint in the table to view the local feature importance values (local explanation) from SHAP's text explainer.", "LocalExplanation": "单个特征重要性", "SelectionCounter": "已选择 {0} 个数据点(共 {1} 个)", "SelectionLimit": "此时最多可选择 5 个数据点。", - "RowCheckboxAriaLabel": "Row checkbox", - "SelectionColumnAriaLabel": "Toggle selection" + "RowCheckboxAriaLabel": "行复选框", + "SelectionColumnAriaLabel": "切换所选内容" }, "IndividualFeatureImportanceView": { - "SmallInstanceSelection": "Instance selection" + "SmallInstanceSelection": "实例选择" }, "MainMenu": { "DashboardSettings": "仪表板配置", @@ -1483,44 +1485,44 @@ "ModelOverview": { "metrics": { "accuracy": { - "name": "Accuracy score", + "name": "准确度分数", "description": "正确分类的数据点的分数。" }, "exactMatchRatio": { - "name": "Exact match ratio", - "description": "The ratio of instances classified correctly for every label." + "name": "完全匹配比率", + "description": "为每个标签正确分类的实例的比率。" }, "meteorScore": { - "name": "Meteor Score", - "description": "METEOR Score is calculated based on the harmonic mean of precision and recall, with recall weighted more than precision in question answering task." + "name": "流星评分", + "description": "METEOR 分数是根据精度和召回率的协调平均值计算得出的，在问题解答任务中，召回率加权超过精度。" }, "bleuScore": { - "name": "Bleu Score", - "description": "Bleu Score measures the ratio of words (and/or n-grams) in the machine generated text that appeared in the reference text in question answering task." + "name": "Bleu 评分", + "description": "Bleu 评分度量在计算机生成的文本中字词(和/或 n-gram)的比率，这些文本出现在问题解答任务的引用文本中。" }, "bertScore": { - "name": "Bert Score", - "description": "BERTScore focuses on computing semantic similarity between tokens of reference and machine generated text in question answering task." + "name": "Bert 评分", + "description": "BERTScore 专注于计算在问题解答任务中引用令牌和计算机生成的文本之间的语义相似性。" }, "rougeScore": { - "name": "Rouge Score", - "description": "Rouge Score measures the ratio of words (and/or n-grams) in the reference text that appeared in the machine generated text in question answering task." + "name": "Rouge 评分", + "description": "Rouge 评分度量在计算机生成的问题解答任务中生成的文本中出现的引用文本中的单词(和/或 n-gram)的比率。" }, "hammingScore": { - "name": "Hamming score", - "description": "The average ratio of labels classified correctly among those classified as 1 in multilabel task." + "name": "汉明评分", + "description": "在多标签任务中分类为 1 的标签中，正确分类的标签的平均比率。" }, "f1Score": { "name": "F1 分数", "description": "F1-分数是精准率和召回率的调和平均值。" }, "f1ScoreMacro": { - "name": "Macro F1 score", - "description": "Macro F1 score is the harmonic mean of precision and recall for each class, with each class weighted equally." + "name": "宏 F1 分数", + "description": "宏 F1 分数是每个类的精度和召回率的协调平均值，每个类加权均等。" }, "f1ScoreMicro": { - "name": "Micro F1 score", - "description": "Micro F1 score is the harmonic mean of precision and recall for each class, with each class weighted according to how many instances it contains." + "name": "微 F1 分数", + "description": "微 F1 分数是每个类的精度和召回率的协调平均值，每个类根据其包含的实例数进行加权。" }, "meanAbsoluteError": { "name": "平均绝对误差", @@ -1535,24 +1537,24 @@ "description": "分类为 1 的数据中正确分类的数据点的分数。" }, "precisionMacro": { - "name": "Macro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted equally." + "name": "宏精度分数", + "description": "在每个类分类为 1，且每个类加权相等的数据点中正确分类的数据点的分数。" }, "precisionMicro": { - "name": "Micro Precision score", - "description": "The fraction of data points classified correctly among those classified as 1 for each class with each class weighted according to how many instances it contains." + "name": "微精度分数", + "description": "根据每个类包含的实例数加权，在分类为 1 的类中正确分类的数据点的分数。" }, "recall": { "name": "召回率分数", "description": "真实标签为 1 的数据中正确分类的数据点的分数。其他名称: 真正率、敏感度。" }, "recallMacro": { - "name": "Macro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted equally." + "name": "宏召回分数", + "description": "在每个类的真正标签为 1，且每个类加权均等的数据点中正确分类的数据点的分数。" }, "recallMicro": { - "name": "Micro Recall score", - "description": "The fraction of data points classified correctly among those whose true label is 1 for each class with each class weighted according to how many instances it contains." + "name": "微召回率分数", + "description": "在每个类的真正标签为 1 的数据点中正确分类的数据点的分数，每个类根据其包含的实例数进行加权。" }, "falsePositiveRate": { "name": "误报率", @@ -1571,32 +1573,32 @@ "description": "所有预测的平均值。" }, "meanAveragePrecision": { - "name": "Mean Average Precision score", - "description": "Mean average precision for object detection models is the average of AP (average precision) across all classes. This evaluates the robustness of your object detection model and encapsulates the tradeoff between precision and recall." + "name": "平均精度分数", + "description": "对象检测模型的平均精度是所有类中 AP 的平均精度(平均精度)。这将评估对象检测模型的可靠性，并封装精度和召回率之间的权衡。" }, "averagePrecision": { - "name": "Average Precision score", - "description": "Average precision for object detection models is calculated for a selected class." + "name": "平均精度分数", + "description": "为所选类计算对象检测模型的平均精度。" }, "averageRecall": { - "name": "Average Recall score", - "description": "Average recall for object detection models is calculated for a selected class." + "name": "平均召回率分数", + "description": "为所选类计算对象检测模型的平均召回率。" }, "fairnessMetricDifference": "差异", "fairnessMetricRatio": "比率" }, "metricsDropdown": "指标", - "metricsTypeDropdown": "Aggregate method", + "metricsTypeDropdown": "聚合方法", "metricTypes": { - "macro": "Macro", - "micro": "Micro" + "macro": "宏", + "micro": "微" }, - "classSelectionDropdown": "Select class(es)", + "classSelectionDropdown": "选择类", "iouThresholdDropdown": { - "name": "IoU Threshold", - "description": "Intersection over Union quantifies the degree of overlap between the prediction and ground truth bounding box of a detected object in an image. For example, setting an IoU threshold of 70% means that a prediction with greater than 70% overlap with ground truth is True, thus influencing the definition of prediction correctness and calculation of other performance metrics.", + "name": "IOU 阈值", + "description": "交并比量化了图像中检测到的物体的预测边界框与真实边界框之间的重叠程度。例如，将 IoU 阈值设置为 70% 意味着与真实边界框有超过 70% 重叠的预测结果将认为是 True，从而影响了对预测准确性和其他性能指标计算方式的定义。", "iconId": "iouThresholdIconId", - "title": "Learn about the IoU threshold" + "title": "了解 IoU 阈值" }, "notAvailable": "N/A", "countColumnHeader": "样本大小", @@ -1608,14 +1610,14 @@ "featuresDropdown": "功能", "metricChartDropdownSelectionHeader": "指标", "probabilityForClassSelectionHeader": "类的概率", - "targetSelectionHeader": "Target", + "targetSelectionHeader": "目标", "metricSelectionDropdownPlaceholder": "选择指标以比较队列。", - "classSelectionDropdownPlaceholder": "Select class name for class-based analysis.", + "classSelectionDropdownPlaceholder": "为基于类的分析选择类名。", "featureSelectionDropdownPlaceholder": "选择要用于基于功能的分析的功能。", "probabilityDistributionPivotItem": "概率分布", - "regressionDistributionPivotItem": "Target distribution", + "regressionDistributionPivotItem": "目标分布", "metricsVisualizationsPivotItem": "指标可视化效果", - "confusionMatrixPivotItem": "Confusion matrix", + "confusionMatrixPivotItem": "混淆矩阵", "disaggregatedAnalysisFeatureSelectionPlaceholder": "选择要生成基于功能的分析的功能。", "tableCountTooltip": "队列 {0} 包含 {1} 个实例。", "tableMetricTooltip": "队列 {1} 上模型的 {0} 为 {2}", @@ -1626,36 +1628,36 @@ "metricSelectionButton": "选择指标", "cohortSelectionButton": "选择队列", "probabilityLabelSelectionButton": "选择标签", - "regressionTargetSelectionButton": "Choose target", + "regressionTargetSelectionButton": "选择目标", "selectAllCohortsOption": "全选", "other": "其他", "BoxPlot": { "outlierProbability": "概率", "outlierLabel": "离群值", "boxPlotSeriesLabel": "箱形图", - "lowerWhisker": "Lower whisker", - "upperWhisker": "Upper whisker", - "median": "Median", - "lowerQuartile": "Lower quartile", - "upperQuartile": "Upper quartile" + "lowerWhisker": "下须", + "upperWhisker": "上须", + "median": "中值", + "lowerQuartile": "下四分位数", + "upperQuartile": "上四分位数" }, "chartConfigApply": "应用", "chartConfigCancel": "取消", "chartConfigDatasetCohortSelectionPlaceholder": "选择数据集队列", "chartConfigFeatureBasedCohortSelectionPlaceholder": "选择基于功能的队列", "confusionMatrix": { - "confusionMatrixCohortSelectionLabel": "Select dataset cohort", - "confusionMatrixClassSelectionLabel": "Select classes", - "confusionMatrixClassMinSelectionError": "Select at least {0} classes to visualize the confusion matrix.", - "confusionMatrixClassMaxSelectionError": "Select at most {0} classes to visualize the confusion matrix.", - "confusionMatrixClassSelectionDefaultPlaceholder": "Choose classes", - "confusionMatrixHeatmapTooltip": "{0} datapoints should be {1}, predicted to be {2}", - "confusionMatrixYAxisLabel": "True Class", - "confusionMatrixXAxisLabel": "Predicted Class", - "class": "Class" + "confusionMatrixCohortSelectionLabel": "选择数据集队列", + "confusionMatrixClassSelectionLabel": "选择类", + "confusionMatrixClassMinSelectionError": "至少选择 {0} 类以可视化混淆矩阵。", + "confusionMatrixClassMaxSelectionError": "最多选择 {0} 类以可视化混淆矩阵。", + "confusionMatrixClassSelectionDefaultPlaceholder": "选择类", + "confusionMatrixHeatmapTooltip": "{0} 数据点应为 {1}，预测为 {2}", + "confusionMatrixYAxisLabel": "真实类", + "confusionMatrixXAxisLabel": "预测类", + "class": "类" }, "nA": "N/A", - "disaggregatedAnalysisBaseCohortDisclaimer": "The cohorts in the following feature-based analysis are based on the global cohort, {0}.", + "disaggregatedAnalysisBaseCohortDisclaimer": "以下基于功能的分析中的队列基于全局队列，{0}。", "disaggregatedAnalysisBaseCohortWarning": "与 {0} 队列不同，{1} 包含筛选器。因此，它只捕获整个数据集的子集，而见解可能不会推广到完整的数据集。", "probabilitySplineChartToggleLabel": "使用自由绘制曲线图", "countAxisLabel": "计数", @@ -1685,76 +1687,76 @@ "flyoutDescription": "可以选择查看数据集队列或功能队列。如果功能队列不可用，则需要先在功能队列视图中选择一个或多个功能。随后会生成功能队列，你可以在此处选择它们。" }, "regressionTargetOptions": { - "predictedY": "Predicted Y", - "trueY": "True Y", - "error": "Error" + "predictedY": "预测的 Y", + "trueY": "真实 Y", + "error": "错误" }, "topLevelDescription": "通过浏览预测值的分布和模型性能指标值来评估模型的性能。使用“数据集队列”选项卡，通过查看其在不同预构建或新建数据集队列之间的性能比较分析来研究你的模型。使用“功能队列”，通过查看其在敏感/非敏感功能子队列之间的性能比较分析来研究你的模型。(例如，不同性别、收入水平的性能)。", - "infoTitle": "Additional information on model overview", + "infoTitle": "有关模型概述的其他信息", "visualDisplayToggleLabel": "显示热度地图", "featureBasedViewDescription": "最多选择两个功能，以查看基于功能的队列(如果已选择一个功能)或相交队列(如果已选择两个功能)的模型性能细分。" }, "TableViewTab": { - "Heading": "View the dataset in a table format for all features and rows." + "Heading": "以表格式查看所有功能和行的数据集。" } }, "Forecasting": { - "target": "Target", - "whatIfForecastingHeader": "What-if analysis", - "forecastHeader": "Forecast analysis", - "whatIfForecastingDescription": "What-if allows you to perturb features for your entire time series and observe how the model's forecast changes.", - "whatIfForecastingChooseTimeSeries": "To start, choose a time series from the options below.", - "forecastDescription": "Forecast analysis compares your model's forecast to the actual values of your time series. To enable what-if analysis, provide a dataset with features.", - "timeSeries": "Time series", - "selectTimeSeries": "Select a time series.", - "singleTimeSeries": "The dataset contains only a single time series '{0}' which has been selected by default.", - "trueY": "True Y", - "baselinePrediction": "Baseline prediction", - "forecastComparisonHeader": "Compare What-if Forecasts", - "forecastComparisonChartTitle": "Forecasts", - "forecastComparisonChartTimeAxisLabel": "Time", + "target": "目标", + "whatIfForecastingHeader": "模拟分析", + "forecastHeader": "预测分析", + "whatIfForecastingDescription": "模拟允许你扰动整个时序的功能，并观察模型的预测变化。", + "whatIfForecastingChooseTimeSeries": "若要开始，请从以下选项中选择时序。", + "forecastDescription": "预测分析将模型的预测与时序的实际值进行比较。若要启用模拟分析，请为数据集提供功能。", + "timeSeries": "时序", + "selectTimeSeries": "选择时序。", + "singleTimeSeries": "数据集仅包含默认已选择的单个时序“{0}”。", + "trueY": "真实 Y", + "baselinePrediction": "基线预测", + "forecastComparisonHeader": "比较模拟预测", + "forecastComparisonChartTitle": "预测", + "forecastComparisonChartTimeAxisLabel": "时间", "Transformations": { - "multiply": "multiply", - "divide": "divide", - "add": "add", - "subtract": "subtract", - "change": "change to" + "multiply": "乘", + "divide": "除", + "add": "加", + "subtract": "减", + "change": "更改为" }, "TransformationCreation": { - "title": "Create what-if scenario", - "nameLabel": "What-if scenario name", - "featureInstructions": "Choose a feature to perturb.", - "operationInstructions": "Choose an operation to apply to the feature.", - "operationDropdownHeader": "Operation", - "featureDropdownHeader": "Feature", - "valueSpinButtonHeader": "Value", - "scenarioNamingInstructionsPlaceholder": "Enter a unique name", - "scenarioNamingInstructions": "Enter a name for your what-if scenario.", - "scenarioNamingCollisionMessage": "This name exists already. Please enter a unique name.", - "scenarioNamingLengthMessage": "The name must be between 1 and 50 characters. The actual length is {0}.", - "scenarioNamingInvalidCharactersMessage": "The name can only contain alphanumeric characters, whitespaces, dashes, or underscores, and needs to start with an alphanumeric character.", - "valueErrorMessage": "For operation {0} please select a value other than {1}.", - "invalidCombinationErrorMessage": "This is identical to an existing what-if scenario. Please change the feature, operation, or value.", - "addTransformationButton": "Add Transformation", - "divisionAndMultiplicationBy": "by" + "title": "创建模拟方案", + "nameLabel": "模拟方案名称", + "featureInstructions": "选择一个要扰动的特征。", + "operationInstructions": "选择要应用于该功能的操作。", + "operationDropdownHeader": "操作", + "featureDropdownHeader": "特征", + "valueSpinButtonHeader": "值", + "scenarioNamingInstructionsPlaceholder": "输入唯一名称", + "scenarioNamingInstructions": "输入模拟方案的名称。", + "scenarioNamingCollisionMessage": "此名称已存在。请输入唯一名称。", + "scenarioNamingLengthMessage": "名称必须介于 1 到 50 个字符之间。实际长度为 {0}。", + "scenarioNamingInvalidCharactersMessage": "名称只能包含字母数字字符、空格、短划线或下划线，并且需要以字母数字字符开头。", + "valueErrorMessage": "对于操作 {0}，请选择除 {1} 以外的值。", + "invalidCombinationErrorMessage": "这与现有的模拟方案相同。请更改功能、操作或值。", + "addTransformationButton": "添加转换", + "divisionAndMultiplicationBy": "执行者" }, "TransformationTable": { - "nameColumnHeader": "Name", - "methodColumnHeader": "Method", - "divisionAndMultiplicationBy": "by ", - "header": "What-if Forecasts ({0})" + "nameColumnHeader": "名称", + "methodColumnHeader": "方法", + "divisionAndMultiplicationBy": "执行者 ", + "header": "模拟预测 ({0})" }, "TimeSeries": { - "apply": "Apply", - "cancel": "Cancel", - "cohortList": "Time series list", - "selectCohort": "Select a time series", - "shiftCohort": "Switch time series", - "shiftCohortDescription": "Select a time series from the time series list. Apply the time series to the dashboard." + "apply": "应用", + "cancel": "取消", + "cohortList": "时序列表", + "selectCohort": "选择时序", + "shiftCohort": "切换时序", + "shiftCohortDescription": "从时序列表中选择时序。将时序应用到仪表板。" }, "TimeSeriesSettings": { - "CohortSettingsDescription": "Time series are pre-defined based on time series identifying columns.", - "CohortSettingsTitle": "Time series settings" + "CohortSettingsDescription": "时序是根据时间序列标识列预先定义的。", + "CohortSettingsTitle": "时序设置" } } } \ No newline at end of file diff --git a/libs/localization/src/lib/en.zh-Hant.json b/libs/localization/src/lib/en.zh-Hant.json index 8a556036d5..b4c0c1dd7d 100644 --- a/libs/localization/src/lib/en.zh-Hant.json +++ b/libs/localization/src/lib/en.zh-Hant.json @@ -906,6 +906,8 @@ "index": "索引", "output": "輸出", "predictedY": "預測的 Y", + "odIncorrect": "Incorrect", + "odCorrect": "Correct", "probabilityLabel": "機率 : {0}", "trueY": "實際的 Y", "xValue": "X 值:", @@ -1147,7 +1149,7 @@ "InterpretText": { "View": { "interpretibilityDashboard": "可解譯性儀表板", - "importantWords": "Show most important words", + "importantWords": "顯示最重要的字詞", "topFeatureList": "熱門特徵清單分析", "allButton": "所有特徵", "negButton": "負特徵", @@ -1162,7 +1164,7 @@ "trueAnswer": "正確答案:", "inputs": "輸入", "outputs": "輸出", - "sliderAriaLabel": "Slider for most important words" + "sliderAriaLabel": "重要字詞的滑桿" }, "Legend": { "featureLegend": "文字特徵圖例", @@ -1195,8 +1197,8 @@ "columnTwo": "索引", "columnThree": "實際的 Y", "columnFour": "預測的 Y", - "columnThreeOD": "Correct", - "columnFourOD": "Incorrect", + "columnThreeOD": "正確", + "columnFourOD": "不正確", "columnFive": "其他中繼資料", "chooseObject": "選擇偵測到的物件", "examples": "範例", @@ -1216,8 +1218,8 @@ "panelInformation": "資訊", "predictedLabel": "預測標記", "predictedY": "預測: ", - "correctDetections": "Correct detections: ", - "incorrectDetections": "Incorrect detections: ", + "correctDetections": "正確的偵測: ", + "incorrectDetections": "不正確的偵測: ", "prefix": "物件: ", "rows": "資料列: ", "search": "搜尋",