brier_score#

ComparisonReport.metrics.brier_score(*, data_source='test', aggregate=('mean', 'std'))[source]#

Compute the Brier score.

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.

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. Ignored when comparison is between EstimatorReport instances

Returns:
pd.DataFrame

The Brier score.

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)
>>> estimator_1 = LogisticRegression(max_iter=10000, random_state=42)
>>> estimator_2 = LogisticRegression(max_iter=10000, random_state=43)
>>> comparison_report = evaluate([estimator_1, estimator_2], X, y, splitter=0.2)
>>> comparison_report.metrics.brier_score()
Estimator         LogisticRegression_1  LogisticRegression_2
Metric
Brier score                   0.036...              0.036...