get

ComparisonReport.metrics.get(name, data_source='test', aggregate=('mean', 'std'), flat_index=False, **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.

flat_indexbool, default=True

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

Returns:

pd.DataFrame

The metric values, or None if the metric is not available.

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.get("precision")
Estimator        LogisticRegression_1  LogisticRegression_2
Metric    Label
Precision 0                  0.901961              0.901961
          1                  0.984127              0.984127