Fix formatting

notebooks/metrics_classification.ipynb

@@ -311,13 +311,13 @@
     "blood when the classifier predicted so or the fraction of people predicted to\n",
     "have given blood out of the total population that actually did so.\n",
     "\n",
-    "The former metric, known as the precision, is defined as TP / (TP + FP) and\n",
+    "The former metric, known as the precision, is defined as `TP / (TP + FP)` and\n",
     "represents how likely the person actually gave blood when the classifier\n",
-    "predicted that they did. The latter, known as the recall, defined as TP / (TP\n",
-    "+ FN) and assesses how well the classifier is able to correctly identify\n",
-    "people who did give blood. We could, similarly to accuracy, manually compute\n",
-    "these values, however scikit-learn provides functions to compute these\n",
-    "statistics."
+    "predicted that they did. The latter, known as the recall, defined as\n",
+    "`TP / (TP + FN)` and assesses how well the classifier is able to correctly\n",
+    "identify people who did give blood. We could, similarly to accuracy,\n",
+    "manually compute these values, however scikit-learn provides functions to\n",
+    "compute these statistics."
    ]
   },
   {
@@ -664,4 +664,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

python_scripts/metrics_classification.py

@@ -188,13 +188,13 @@
 # blood when the classifier predicted so or the fraction of people predicted to
 # have given blood out of the total population that actually did so.
 #
-# The former metric, known as the precision, is defined as TP / (TP + FP) and
+# The former metric, known as the precision, is defined as `TP / (TP + FP)` and
 # represents how likely the person actually gave blood when the classifier
-# predicted that they did. The latter, known as the recall, defined as TP / (TP
-# + FN) and assesses how well the classifier is able to correctly identify
-# people who did give blood. We could, similarly to accuracy, manually compute
-# these values, however scikit-learn provides functions to compute these
-# statistics.
+# predicted that they did. The latter, known as the recall, defined as
+# `TP / (TP + FN)` and assesses how well the classifier is able to correctly
+# identify people who did give blood. We could, similarly to accuracy,
+# manually compute these values, however scikit-learn provides functions to
+# compute these statistics.
 
 # %%
 from sklearn.metrics import precision_score, recall_score