sklearn.model_selection.cross_val_score

sklearn.model_selection.cross_val_score(estimator, X, y=None, groups=None, scoring=None, cv=’warn’, n_jobs=None, verbose=0, fit_params=None, pre_dispatch=‘2*n_jobs’, error_score=’raise-deprecating’)[source]

Evaluate a score by cross-validation

Read more in the User Guide.

Parameters:
estimator : estimator object implementing ‘fit’

The object to use to fit the data.

X : array-like

The data to fit. Can be for example a list, or an array.

y : array-like, optional, default: None

The target variable to try to predict in the case of supervised learning.

groups : array-like, with shape (n_samples,), optional

Group labels for the samples used while splitting the dataset into train/test set.

scoring : string, callable or None, optional, default: None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).

cv : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

  • None, to use the default 3-fold cross validation,
  • integer, to specify the number of folds in a (Stratified)KFold,
  • An object to be used as a cross-validation generator.
  • An iterable yielding train, test splits.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

Changed in version 0.20: cv default value if None will change from 3-fold to 5-fold in v0.22.

n_jobs : int or None, optional (default=None)

The number of CPUs to use to do the computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

verbose : integer, optional

The verbosity level.

fit_params : dict, optional

Parameters to pass to the fit method of the estimator.

pre_dispatch : int, or string, optional

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 string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
error_score : ‘raise’ | ‘raise-deprecating’ or numeric

Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If set to ‘raise-deprecating’, a FutureWarning is printed before 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. Default is ‘raise-deprecating’ but from version 0.22 it will change to np.nan.

Returns:
scores : array of float, shape=(len(list(cv)),)

Array of scores of the estimator for each run of the cross validation.

See also

sklearn.model_selection.cross_validate
To run cross-validation on multiple metrics and also to return train scores, fit times and score times.
sklearn.model_selection.cross_val_predict
Get predictions from each split of cross-validation for diagnostic purposes.
sklearn.metrics.make_scorer
Make a scorer from a performance metric or loss function.

Examples

>>> from sklearn import datasets, linear_model
>>> from sklearn.model_selection import cross_val_score
>>> diabetes = datasets.load_diabetes()
>>> X = diabetes.data[:150]
>>> y = diabetes.target[:150]
>>> lasso = linear_model.Lasso()
>>> print(cross_val_score(lasso, X, y, cv=3))  
[0.33150734 0.08022311 0.03531764]