Create 907. Evaluate classification model performance.py

Machine Learning/907. Evaluate classification model performance.py

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+'''
+You are given a list of ground truth labels y_true_list and a list of predicted labels y_pred_list for multiple classes.
+Write a function that takes these two lists as input and returns a pandas DataFrame containing evaluation metrics for each class.
+The function should calculate the ROC AUC score, precision, and recall for each class. The input lists may contain multiple elements,
+each corresponding to a different class. The function should return a DataFrame with one row per class and three columns containing the
+evaluation metrics.
+
+Example 1:
+
+Input:
+y_true_list = [[0, 1, 1, 0, 1],
+[1, 1, 0, 0],
+[0, 0, 1, 1, 0, 1]]
+y_pred_list = [[0, 1, 0, 0, 1],
+[1, 1, 1, 0],
+[0, 0, 1, 0, 1, 1]]
+
+Output:
+
+ROC AUC Score Precision Recall
+0 0.833333 0.866667 0.800000
+1 0.750000 0.833333 0.750000
+2 0.666667 0.666667 0.666667
+
+Example 2:
+
+Input:
+
+y_true_list = [[1, 0, 0, 1, 1],
+[0, 1, 0, 1],
+[1, 1, 0, 0, 1, 0]]
+y_pred_list = [[1, 0, 1, 1, 1],
+[0, 1, 0, 0],
+[1, 0, 0, 1, 1, 1]]
+Output:
+
+ROC AUC Score Precision Recall
+0 0.75 0.850000 0.80
+1 0.75 0.833333 0.75
+2 0.50 0.500000 0.50
+
+Constraints: The output should be in the pandas DataFrame.
+
+'''
+
+# Solution:
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