EstimatorReport.metrics.report_metrics#

EstimatorReport.metrics.report_metrics(*, data_source='test', X=None, y=None, scoring=None, scoring_names=None, pos_label=None, scoring_kwargs=None)[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 provided X and y to compute the metric.

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.

pos_labelint, float, bool or str, default=None

The positive class.

scoring_kwargsdict, default=None

The keyword arguments to pass to the scoring functions.

Returns:

pd.DataFrame: The statistics for the metrics.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import train_test_split
>>> from skore import EstimatorReport
>>> X_train, X_test, y_train, y_test = train_test_split(
...     *load_breast_cancer(return_X_y=True), random_state=0
... )
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(
...     classifier,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> report.metrics.report_metrics(pos_label=1)
Metric              Precision (↗︎)  Recall (↗︎)  ROC AUC (↗︎)  Brier score (↘︎)
LogisticRegression        0.98...     0.93...      0.99...          0.03...