CrossValidationReport.metrics.precision#

CrossValidationReport.metrics.precision(*, data_source='test', average=None, pos_label=None, aggregate=None)[source]#

Compute the precision 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 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.

  • “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 or str, default=None

The positive class.

aggregate{“mean”, “std”} or list of such str, default=None

Function to aggregate the scores across the cross-validation splits.

Returns:
pd.DataFrame

The precision score.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import CrossValidationReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
>>> report.metrics.precision()
                            LogisticRegression
                                        Split #0  Split #1
Metric         Label / Average
Precision     0                         0.96...   0.90...
              1                         0.93...   0.96...