CrossValidationReport.metrics.r2#

CrossValidationReport.metrics.r2(*, data_source='test', X=None, y=None, multioutput='raw_values', aggregate=('mean', 'std'))[source]#

Compute the R² 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.

multioutput{“raw_values”, “uniform_average”} or array-like of shape (n_outputs,), default=”raw_values”

Defines aggregating of multiple output values. Array-like value defines weights used to average errors. The other possible values are:

  • “raw_values”: Returns a full set of errors in case of multioutput input.

  • “uniform_average”: Errors of all outputs are averaged with uniform weight.

By default, no averaging is done.

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 R² score.

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from skore import CrossValidationReport
>>> X, y = load_diabetes(return_X_y=True)
>>> regressor = Ridge()
>>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
>>> report.metrics.r2()
        Ridge
            mean       std
Metric
R²      0.37...  0.02...