summarize#

CrossValidationReport.metrics.summarize(*, data_source='test', metric=None, metric_kwargs=None, response_method=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, callable, scorer, or list of such instances or dict of such instances, default=None

The metric(s) to report. The possible values are:

if a string, either one of the built-in metrics or a scikit-learn scorer name. You can get the possible list of string using report.metrics.help() or sklearn.metrics.get_scorer_names() for the built-in metrics or the scikit-learn scorers, respectively.
if 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 metric_kwargs. No favorability indicator can be displayed in this case.
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(). In this case, the metric favorability will only be displayed if it is given explicitly via make_scorer’s greater_is_better parameter.
if a dict, the keys are used as metric names and the values are the metric functions (strings, callables, or scorers as described above).
if a list, each element can be any of the above types (strings, callables, scorers).

metric_kwargsdict, default=None

The keyword arguments to pass to the metric functions.

response_method{“predict”, “predict_proba”, “predict_log_proba”, “decision_function”} or list of such str, default=None

The estimator’s method to be invoked to get the predictions. Only necessary for custom metrics.

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 CrossValidationReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(
...     classifier, X=X, y=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...         (↗︎)

summarize#

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