ComparisonReport.metrics.recall#

ComparisonReport.metrics.recall(*, data_source='test', X=None, y=None, average=None, pos_label=<DEFAULT>, aggregate=('mean', 'std'))[source]#

Compute the recall 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.

  • “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.

average{“binary”,”macro”, “micro”, “weighted”, “samples”} or None, default=None

Used with multiclass problems. If None, the metrics for each class are returned. Otherwise, this determines the type of averaging performed on the data:

  • “binary”: Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.

  • “micro”: Calculate metrics globally by counting the total true positives, false negatives and false positives.

  • “macro”: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.

  • “weighted”: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall. Weighted recall is equal to accuracy.

  • “samples”: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from accuracy_score()).

Note

If pos_label is specified and average is None, then we report only the statistics of the positive class (i.e. equivalent to average="binary").

pos_labelint, float, bool, str or None, default=_DEFAULT

The label to consider as the positive class when computing the metric. Use this parameter to override the positive class. By default, the positive class is set to the one provided when creating the report. If None, the metric is computed considering each class as a positive class.

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 recall score.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import ComparisonReport, EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=42, as_dict=True)
>>> estimator_1 = LogisticRegression(max_iter=10000, random_state=42)
>>> estimator_report_1 = EstimatorReport(estimator_1, **split_data)
>>> estimator_2 = LogisticRegression(max_iter=10000, random_state=43)
>>> estimator_report_2 = EstimatorReport(estimator_2, **split_data)
>>> comparison_report = ComparisonReport(
...     [estimator_report_1, estimator_report_2]
... )
>>> comparison_report.metrics.recall()
Estimator                    LogisticRegression_1  LogisticRegression_2
Metric      Label / Average
Recall                    0              0.944...              0.944...
                          1              0.977...              0.977...