get

CrossValidationReport.metrics.get(name, data_source='test', aggregate=('mean', 'std'), **kwargs)

Get a metric value.

Parameters:

namestr

Name of the metric to compute. Get all available metrics with available().

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.

Returns:

pd.DataFrame

The metric values.

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)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = evaluate(classifier, X, y, splitter=2)
>>> report.metrics.get("precision")
Estimator       LogisticRegression
Aggregate                     mean       std
Metric    Label
Precision 0               0.93...  0.04...
          1               0.94...  0.02...