sklearn.model_selection
.permutation_test_score¶

sklearn.model_selection.
permutation_test_score
(estimator, X, y, *, groups=None, cv=None, n_permutations=100, n_jobs=None, random_state=0, verbose=0, scoring=None, fit_params=None)[source]¶ Evaluate the significance of a crossvalidated score with permutations
Permutes targets to generate ‘randomized data’ and compute the empirical pvalue against the null hypothesis that features and targets are independent.
The pvalue represents the fraction of randomized data sets where the estimator performed as well or better than in the original data. A small pvalue suggests that there is a real dependency between features and targets which has been used by the estimator to give good predictions. A large pvalue may be due to lack of real dependency between features and targets or the estimator was not able to use the dependency to give good predictions.
Read more in the User Guide.
 Parameters
 estimatorestimator object implementing ‘fit’
The object to use to fit the data.
 Xarraylike of shape at least 2D
The data to fit.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs) or None
The target variable to try to predict in the case of supervised learning.
 groupsarraylike of shape (n_samples,), default=None
Labels to constrain permutation within groups, i.e.
y
values are permuted among samples with the same group identifier. When not specified,y
values are permuted among all samples.When a grouped crossvalidator is used, the group labels are also passed on to the
split
method of the crossvalidator. The crossvalidator uses them for grouping the samples while splitting the dataset into train/test set. scoringstr or callable, 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.
If None the estimator’s score method is used.
 cvint, crossvalidation generator or an iterable, default=None
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
None, to use the default 5fold cross validation,
int, to specify the number of folds in a
(Stratified)KFold
,An iterable yielding (train, test) splits as arrays of indices.
For int/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 crossvalidation strategies that can be used here.
Changed in version 0.22:
cv
default value if None changed from 3fold to 5fold. n_permutationsint, default=100
Number of times to permute
y
. n_jobsint, default=None
Number of jobs to run in parallel. Training the estimator and computing the crossvalidated score are parallelized over the permutations.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. random_stateint, RandomState instance or None, default=0
Pass an int for reproducible output for permutation of
y
values among samples. See Glossary. verboseint, default=0
The verbosity level.
 fit_paramsdict, default=None
Parameters to pass to the fit method of the estimator.
New in version 0.24.
 Returns
 scorefloat
The true score without permuting targets.
 permutation_scoresarray of shape (n_permutations,)
The scores obtained for each permutations.
 pvaluefloat
The pvalue, which approximates the probability that the score would be obtained by chance. This is calculated as:
(C + 1) / (n_permutations + 1)
Where C is the number of permutations whose score >= the true score.
The best possible pvalue is 1/(n_permutations + 1), the worst is 1.0.
Notes
This function implements Test 1 in:
Ojala and Garriga. Permutation Tests for Studying Classifier Performance. The Journal of Machine Learning Research (2010) vol. 11