recall#

ComparisonReport.metrics.recall(*, data_source='test', average=None, 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.

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

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 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.recall()
Estimator                    LogisticRegression_1  LogisticRegression_2
Metric      Label / Average
Recall                    0              0.978...              0.978...
                          1              0.925...              0.925...