CrossValidationReport.metrics.plot.precision_recall#

CrossValidationReport.metrics.plot.precision_recall(*, data_source='test', pos_label=None, ax=None)[source]#

Plot the precision-recall curve.

Parameters:

data_source{“test”, “train”, “X_y”}, 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.

pos_labelint, float, bool or str, default=None

The positive class.

axmatplotlib.axes.Axes, default=None

The axes to plot on.

Returns:

PrecisionRecallCurveDisplay: The precision-recall curve display.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import CrossValidationReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> display = report.metrics.plot.precision_recall()
Computing predictions for display ...
>>> display.plot()