EstimatorReport.metrics.brier_score#

EstimatorReport.metrics.brier_score(*, data_source='test', X=None, y=None)[source]#

Compute the Brier score.

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.

Returns:
float

The Brier score.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(classifier, **split_data)
>>> report.metrics.brier_score()
0.03...