summarize#

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

Report a set of metrics for our estimator.

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 and present them side-by-side.

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=0.2, pos_label=1)
>>> report.metrics.summarize().frame(favorability=True).drop(
...    ["Fit time (s)", "Predict time (s)"]
... )
            LogisticRegression Favorability
Metric
Accuracy               0.94...         (↗︎)
Precision              0.98...         (↗︎)
Recall                 0.92...         (↗︎)
ROC AUC                0.99...         (↗︎)
Log loss               0.11...         (↘︎)
Brier score            0.03...         (↘︎)
>>> # Using scikit-learn metrics
>>> report.metrics.summarize(metric="log_loss").frame(favorability=True)
          LogisticRegression Favorability
Metric
Log loss             0.11...          (↘︎)
>>> report.metrics.summarize(
...    data_source="both"
... ).frame(favorability=True).drop(["Fit time (s)", "Predict time (s)"])
             LogisticRegression (train)  LogisticRegression (test)  Favorability
Metric
Accuracy                        0.96...                     0.94...          (↗︎)
Precision                       0.96...                     0.98...          (↗︎)
Recall                          0.97...                     0.92...          (↗︎)
ROC AUC                         0.99...                     0.99...          (↗︎)
Log loss                        0.08...                     0.11...          (↘︎)
Brier score                     0.02...                     0.03...          (↘︎)