sklearn.model_selection.cross_validate

sklearn.model_selection.cross_validate(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch=‘2*n_jobs’, return_train_score=True)[source]

Evaluate metric(s) by cross-validation and also record fit/score times.

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, list/tuple, dict or None, default: None

A single string (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 default scorer (if available) is used.

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.

n_jobs : integer, optional

The number of CPUs to use to do the computation. -1 means ‘all CPUs’.

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’

return_train_score : boolean, default True

Whether to include train scores in the return dict if scoring is of multimetric type.

Returns:

scores : dict 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 dict are:

test_score

The score array for test scores on each cv split.

train_score

The score array for train scores on each cv split. This is available only if return_train_score parameter is True.

fit_time

The time for fitting the estimator on the train set for each cv split.

score_time

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_score is set to True

See also

sklearn.model_selection.cross_val_score
Run cross-validation for single metric evaluation.
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_validate
>>> from sklearn.metrics.scorer 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 cross_validate

>>> cv_results = cross_validate(lasso, X, y, return_train_score=False)
>>> sorted(cv_results.keys())                         
['fit_time', 'score_time', 'test_score']
>>> cv_results['test_score']    
array([ 0.33...,  0.08...,  0.03...])

Multiple metric evaluation using cross_validate (please refer the scoring parameter doc for more information)

>>> scores = cross_validate(lasso, X, y,
...                         scoring=('r2', 'neg_mean_squared_error'))
>>> print(scores['test_neg_mean_squared_error'])      
[-3635.5... -3573.3... -6114.7...]
>>> print(scores['train_r2'])                         
[ 0.28...  0.39...  0.22...]