evaluate#

skore.evaluate(estimator, X, y, *, splitter=0.2, pos_label=None, n_jobs=None)[source]#

Evaluate one or more estimators on the given data.

Passing several estimators provides a report to compare them, while the splitter parameter controls whether a train-test split or cross-validation is used.

Parameters:

estimatorestimator object or list of estimator objects

A scikit-learn compatible estimator, or a list of such estimators to compare.

Xarray-like of shape (n_samples, n_features) or list of array-like

Feature matrix. When estimator is a list, X can also be a list of feature matrices (one per estimator) to e.g. compare models with different preprocessing pipelines.

yarray-like of shape (n_samples,)

Target vector.

splitterfloat, int, str, or cross-validation object, default=0.2

Determines how the data is split:

float: perform a single train-test split where the data is shuffled before splitting with a fixed seed (random_state=0) for reproducibility. Pass a TrainTestSplit instance for more control over the splitting parameters.
"prefit": the estimator is assumed to be already fitted; X and y are used as the test set.
int: number of folds for cross-validation (passed to CrossValidationReport).
cross-validation splitter (e.g. KFold, StratifiedKFold): passed directly to CrossValidationReport.

pos_labelint, float, bool or str, default=None

The positive class label for binary classification metrics. Forwarded to the underlying report.

n_jobsint or None, default=None

Number of jobs for parallel execution. Forwarded to CrossValidationReport or ComparisonReport.

Returns:

reportEstimatorReport, CrossValidationReport or ComparisonReport: The report corresponding to the evaluation strategy.

Raises:

ValueError: If splitter is a string other than "prefit".

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import evaluate
>>> X, y = make_classification(random_state=42)

Default 80/20 train-test split:

>>> report = evaluate(LogisticRegression(), X, y)

Cross-validation with 5 folds:

>>> report = evaluate(LogisticRegression(), X, y, splitter=5)

Evaluate a pre-fitted estimator:

>>> from sklearn.model_selection import train_test_split
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
>>> fitted_model = LogisticRegression().fit(X_train, y_train)
>>> report = evaluate(fitted_model, X_test, y_test, splitter="prefit")