DOC Add examples to PrecisionRecall and ConfusionMatrix Display (scikit-learn#17492)

pardeep-singh · viclafargue · commit cce77633a69d · 2020-06-26T11:43:36.000Z
diff --git a/sklearn/metrics/_plot/confusion_matrix.py b/sklearn/metrics/_plot/confusion_matrix.py
@@ -40,6 +40,24 @@ class ConfusionMatrixDisplay:
 
     figure_ : matplotlib Figure
         Figure containing the confusion matrix.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+    >>> from sklearn.model_selection import train_test_split
+    >>> from sklearn.svm import SVC
+    >>> X, y = make_classification(random_state=0)
+    >>> X_train, X_test, y_train, y_test = train_test_split(X, y,
+    ...                                                     random_state=0)
+    >>> clf = SVC(random_state=0)
+    >>> clf.fit(X_train, y_train)
+    SVC(random_state=0)
+    >>> predictions = clf.predict(X_test)
+    >>> cm = confusion_matrix(y_test, predictions, labels=clf.classes_)
+    >>> disp = ConfusionMatrixDisplay(confusion_matrix=cm,
+    ...                               display_labels=clf.classes_)
+    >>> disp.plot() # doctest: +SKIP
     """
     def __init__(self, confusion_matrix, *, display_labels=None):
         self.confusion_matrix = confusion_matrix
diff --git a/sklearn/metrics/_plot/precision_recall_curve.py b/sklearn/metrics/_plot/precision_recall_curve.py
@@ -40,6 +40,24 @@ class PrecisionRecallDisplay:
 
     figure_ : matplotlib Figure
         Figure containing the curve.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.metrics import (precision_recall_curve,
+    ...                              PrecisionRecallDisplay)
+    >>> from sklearn.model_selection import train_test_split
+    >>> from sklearn.svm import SVC
+    >>> X, y = make_classification(random_state=0)
+    >>> X_train, X_test, y_train, y_test = train_test_split(X, y,
+    ...                                                     random_state=0)
+    >>> clf = SVC(random_state=0)
+    >>> clf.fit(X_train, y_train)
+    SVC(random_state=0)
+    >>> predictions = clf.predict(X_test)
+    >>> precision, recall, _ = precision_recall_curve(y_test, predictions)
+    >>> disp = PrecisionRecallDisplay(precision=precision, recall=recall)
+    >>> disp.plot() # doctest: +SKIP
     """
     def __init__(self, precision, recall, *,
                  average_precision=None, estimator_name=None):
