EstimatorReport.cache_predictions

EstimatorReport.cache_predictions(response_methods='auto', n_jobs=None)

Cache estimator’s predictions.

Parameters:

response_methods“auto” or list of str, default=”auto”

The response methods to precompute. If “auto”, the response methods are inferred from the ml task: for classification we compute the response of the predict_proba, decision_function and predict methods; for regression we compute the response of the predict method.

n_jobsint or None, default=None

The number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

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
{...}