EstimatorReport.get_predictions#

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

The predictions.

Raises:
ValueError

If the data source is invalid.

Examples

>>> from sklearn.datasets import make_classification
>>> from skore import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> X, y = make_classification(random_state=42)
>>> split_data = train_test_split(X=X, y=y, random_state=42, as_dict=True)
>>> estimator = LogisticRegression()
>>> from skore import EstimatorReport
>>> report = EstimatorReport(estimator, **split_data)
>>> predictions = report.get_predictions(data_source="test")
>>> predictions.shape
(25,)

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