EstimatorReport.get_predictions#

EstimatorReport.get_predictions(*, data_source, response_method='predict', X=None, pos_label=<DEFAULT>)[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”, “X_y”}, 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.

  • “X_y” : use the provided X and y to compute the predictions.

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

The response method to use to get the predictions.

Xarray-like of shape (n_samples, n_features), optional

When data_source is “X_y”, the input features on which to compute the response method.

pos_labelint, float, bool, str or None, default=_DEFAULT

The label to consider as the positive class when computing predictions in binary classification cases. By default, the positive class is set to the one provided when creating the report. If None, estimator_.classes_[1] is used as positive label.

When pos_label is equal to estimator_.classes_[0], it will be equivalent to estimator_.predict_proba(X)[:, 0] for response_method="predict_proba" and -estimator_.decision_function(X) for response_method="decision_function".

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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