roc#

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

Plot the ROC 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:
RocCurveDisplay

The ROC curve display.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import ComparisonReport, EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=42, as_dict=True)
>>> estimator_1 = LogisticRegression(max_iter=10000, random_state=42)
>>> estimator_report_1 = EstimatorReport(estimator_1, **split_data)
>>> estimator_2 = LogisticRegression(max_iter=10000, random_state=43)
>>> estimator_report_2 = EstimatorReport(estimator_2, **split_data)
>>> comparison_report = ComparisonReport(
...     [estimator_report_1, estimator_report_2]
... )
>>> display = comparison_report.metrics.roc()
>>> display.plot()