10000 DOC Add examples to PrecisionRecall and ConfusionMatrix Display by pardeep-singh · Pull Request #17492 · scikit-learn/scikit-learn · GitHub
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DOC Add examples to PrecisionRecall and ConfusionMatrix Display #17492

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Merged
merged 7 commits into from
Jun 7, 2020
Merged

DOC Add examples to PrecisionRecall and ConfusionMatrix Display #17492

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a129ab4
Add example to ConfusionMatrixDisplay class documentation
pardeep-singh Jun 6, 2020
5e342ce
Add example to PrecisionRecallDisplay class documentation
pardeep-singh Jun 6, 2020
5a61323
Make Code Review Fixes
pardeep-singh Jun 7, 2020
781f44b
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn…
pardeep-singh Jun 7, 2020
cdcff01
Refactor Metrics examples based on Code Review
pardeep-singh Jun 7, 2020
085a1e0
Fix formatting issues in metrics examples
pardeep-singh Jun 7, 2020
776bea7
Add return value to metrics doc examples
pardeep-singh Jun 7, 2020
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18 changes: 18 additions & 0 deletions sklearn/metrics/_plot/confusion_matrix.py
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Expand Up @@ -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
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18 changes: 18 additions & 0 deletions sklearn/metrics/_plot/precision_recall_curve.py
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Expand Up @@ -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):
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