precision_recall#

ComparisonReport.metrics.precision_recall(*, data_source='test')[source]#

Plot the precision-recall curve.

Parameters:
data_source{“test”, “train”, “both”}, default=”test”

The data source to use.

  • “test” : use the test set provided when creating the report.

  • “train” : use the train set provided when creating the report.

  • “both” : use both the train and test sets to compute the metrics.

Returns:
PrecisionRecallCurveDisplay

The precision-recall curve display.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import evaluate
>>> X, y = load_breast_cancer(return_X_y=True)
>>> estimator_1 = LogisticRegression(max_iter=10000, random_state=42)
>>> estimator_2 = LogisticRegression(max_iter=10000, random_state=43)
>>> comparison_report = evaluate([estimator_1, estimator_2], X, y, splitter=0.2)
>>> display = comparison_report.metrics.precision_recall()
>>> display.plot()