sklearn.cross_validation.permutation_test_score

sklearn.cross_validation.permutation_test_score(estimator, X, y, cv=None, n_permutations=100, n_jobs=1, labels=None, random_state=0, verbose=0, scoring=None) [source]

Evaluate the significance of a cross-validated score with permutations

Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.permutation_test_score instead.

Read more in the User Guide.

Parameters:

estimator : estimator object implementing ‘fit’ The object to use to fit the data. X : array-like of shape at least 2D The data to fit. y : array-like The target variable to try to predict in the case of supervised learning. 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. 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_permutations : integer, optional Number of times to permute y. n_jobs : integer, optional The number of CPUs to use to do the computation. -1 means ‘all CPUs’. labels : array-like of shape [n_samples] (optional) Labels constrain the permutation among groups of samples with a same label. random_state : int, RandomState instance or None, optional (default=0) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. verbose : integer, optional The verbosity level.

Returns:

score : float The true score without permuting targets. permutation_scores : array, shape (n_permutations,) The scores obtained for each permutations. pvalue : float The p-value, 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 p-value 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