ComparisonReport.get_predictions#

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

Get predictions from the underlying reports.

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) or list of such lists

The predictions for each EstimatorReport or CrossValidationReport.

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
>>> from skore import ComparisonReport, EstimatorReport
>>> X, y = make_classification(random_state=42)
>>> split_data = train_test_split(X=X, y=y, random_state=42, as_dict=True)
>>> estimator_1 = LogisticRegression()
>>> estimator_report_1 = EstimatorReport(estimator_1, **split_data)
>>> estimator_2 = LogisticRegression(C=2)  # Different regularization
>>> estimator_report_2 = EstimatorReport(estimator_2, **split_data)
>>> report = ComparisonReport([estimator_report_1, estimator_report_2])
>>> report.cache_predictions()
>>> predictions = report.get_predictions(data_source="test")
>>> print([split_predictions.shape for split_predictions in predictions])
[(25,), (25,)]

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