EstimatorReport.cache_predictions

EstimatorReport.cache_predictions(data_source='both')

Cache estimator’s predictions.

Parameters:

data_source{“test”, “train”, “both”}, default=”both”

The data source(s) for which to precompute predictions.

• “test” : cache predictions for the test set only.

• “train” : cache predictions for the train set only.

• “both” : cache predictions for both train and test sets when available.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(classifier, **split_data)
>>> report.cache_predictions()
>>> report._cache
{...}