summarize#

CrossValidationReport.metrics.summarize(*, data_source='test', metric=None)[source]#

Report a set of metrics for our estimator.

Parameters:

data_source{“test”, “train”}, 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.

metricstr or list of str or None, default=None

The metrics to report, from the list of registered metrics. None means show all registered metrics. To add a custom metric, see add().

Returns:

MetricsSummaryDisplay: A display containing the statistics for the metrics.

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)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = evaluate(
...     classifier, X, y, splitter=2, pos_label=1
... )
>>> report.metrics.summarize(
...     metric=["precision", "recall"],
... ).frame(flat_index=False, favorability=True)
          LogisticRegression           Favorability
                        mean       std
Metric
Precision           0.94...  0.02...         (↗︎)
Recall              0.96...  0.02...         (↗︎)