CrossValidationReport.metrics.report_metrics#

CrossValidationReport.metrics.report_metrics(*, data_source='test', X=None, y=None, scoring=None, scoring_names=None, scoring_kwargs=None, pos_label=None, indicator_favorability=False, flat_index=False, aggregate=('mean', 'std'))[source]#

Report a set of metrics for our estimator.

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.

  • “X_y” : use the train set provided when creating the report and the target variable.

Xarray-like of shape (n_samples, n_features), default=None

New data on which to compute the metric. By default, we use the validation set provided when creating the report.

yarray-like of shape (n_samples,), default=None

New target on which to compute the metric. By default, we use the target provided when creating the report.

scoringlist of str, callable, or scorer, default=None

The metrics to report. You can get the possible list of string by calling report.metrics.help(). When passing a callable, it should take as arguments y_true, y_pred as the two first arguments. Additional arguments can be passed as keyword arguments and will be forwarded with scoring_kwargs. If the callable API is too restrictive (e.g. need to pass same parameter name with different values), you can use scikit-learn scorers as provided by sklearn.metrics.make_scorer().

scoring_nameslist of str, default=None

Used to overwrite the default scoring names in the report. It should be of the same length as the scoring parameter.

scoring_kwargsdict, default=None

The keyword arguments to pass to the scoring functions.

pos_labelint, float, bool or str, default=None

The positive class.

indicator_favorabilitybool, default=False

Whether or not to add an indicator of the favorability of the metric as an extra column in the returned DataFrame.

flat_indexbool, default=False

Whether to flatten the MultiIndex columns. Flat index will always be lower case, do not include spaces and remove the hash symbol to ease indexing.

aggregate{“mean”, “std”}, list of such str or None, default=(“mean”, “std”)

Function to aggregate the scores across the cross-validation splits. None will return the scores for each split.

Returns:
pd.DataFrame

The statistics for the metrics.

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)
>>> report.metrics.report_metrics(
...     scoring=["precision", "recall"],
...     pos_label=1,
...     indicator_favorability=True,
... )
          LogisticRegression           Favorability
                        mean       std
Metric
Precision           0.94...  0.02...         (↗︎)
Recall              0.96...  0.02...         (↗︎)