cross_validate(estimator, X, y=None, *, groups=None, scoring=None, cv=None, n_jobs=None, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', return_train_score=False, return_estimator=False, error_score=nan)¶
Evaluate metric(s) by cross-validation and also record fit/score times.
Read more in the User Guide.
- estimatorestimator object implementing ‘fit’
The object to use to fit the data.
- Xarray-like of shape (n_samples, n_features)
The data to fit. Can be for example a list, or an array.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
The target variable to try to predict in the case of supervised learning.
- groupsarray-like of shape (n_samples,), default=None
- scoringstr, callable, list/tuple, or dict, default=None
A single str (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set.
For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values.
NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each.
See Specifying multiple metrics for evaluation for an example.
If None, the estimator’s score method is used.
- cvint, cross-validation generator or an iterable, default=None
Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the default 5-fold cross validation,
int, to specify the number of folds in a
An iterable yielding (train, test) splits as arrays of indices.
Refer User Guide for the various cross-validation strategies that can be used here.
Changed in version 0.22:
cvdefault value if None changed from 3-fold to 5-fold.
- n_jobsint, default=None
- verboseint, default=0
The verbosity level.
- fit_paramsdict, default=None
Parameters to pass to the fit method of the estimator.
- pre_dispatchint or str, default=’2*n_jobs’
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
An int, giving the exact number of total jobs that are spawned
A str, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
- return_train_scorebool, default=False
Whether to include train scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance.
New in version 0.19.
Changed in version 0.21: Default value was changed from
- return_estimatorbool, default=False
Whether to return the estimators fitted on each split.
New in version 0.20.
- error_score‘raise’ or numeric
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.
New in version 0.20.
- scoresdict of float arrays of shape (n_splits,)
Array of scores of the estimator for each run of the cross validation.
A dict of arrays containing the score/time arrays for each scorer is returned. The possible keys for this
The score array for test scores on each cv split. Suffix
test_scorechanges to a specific metric like
test_aucif there are multiple scoring metrics in the scoring parameter.
The score array for train scores on each cv split. Suffix
train_scorechanges to a specific metric like
train_aucif there are multiple scoring metrics in the scoring parameter. This is available only if
The time for fitting the estimator on the train set for each cv split.
The time for scoring the estimator on the test set for each cv split. (Note time for scoring on the train set is not included even if
return_train_scoreis set to
The estimator objects for each cv split. This is available only if
return_estimatorparameter is set to
>>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_validate >>> from sklearn.metrics import make_scorer >>> from sklearn.metrics import confusion_matrix >>> from sklearn.svm import LinearSVC >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso()
Single metric evaluation using
>>> cv_results = cross_validate(lasso, X, y, cv=3) >>> sorted(cv_results.keys()) ['fit_time', 'score_time', 'test_score'] >>> cv_results['test_score'] array([0.33150734, 0.08022311, 0.03531764])
Multiple metric evaluation using
cross_validate(please refer the
scoringparameter doc for more information)
>>> scores = cross_validate(lasso, X, y, cv=3, ... scoring=('r2', 'neg_mean_squared_error'), ... return_train_score=True) >>> print(scores['test_neg_mean_squared_error']) [-3635.5... -3573.3... -6114.7...] >>> print(scores['train_r2']) [0.28010158 0.39088426 0.22784852]