CrossValidationReport.get_predictions#

CrossValidationReport.get_predictions(*, data_source, response_method='predict')[source]

Get estimator’s predictions.

This method has the advantage to reload from the cache if the predictions were already computed in a previous call.

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.

response_method{“predict”, “predict_proba”, “decision_function”}, default=”predict”

The response method to use to get the predictions.

Returns:
list of np.ndarray of shape (n_samples,) or (n_samples, n_classes)

The predictions for each cross-validation split.

Raises:
ValueError

If the data source is invalid.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> X, y = make_classification(random_state=42)
>>> estimator = LogisticRegression()
>>> from skore import CrossValidationReport
>>> report = CrossValidationReport(estimator, X=X, y=y, splitter=2)
>>> predictions = report.get_predictions(data_source="test")
>>> print([split_predictions.shape for split_predictions in predictions])
[(50,), (50,)]

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