accuracy#

CrossValidationReport.metrics.accuracy(*, data_source='test', aggregate=('mean', 'std'), flat_index=False)[source]#

Compute the accuracy 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.

flat_indexbool, default=True

Whether to return a flat index or a multi-index.

Returns:
pd.DataFrame

The accuracy score.

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)
>>> report.metrics.accuracy(flat_index=False)
        LogisticRegression
                    mean      std
Metric
Accuracy           0.94...  0.00...