DOC Add doc for permutation_test_score
@@ -1039,9 +1039,19 @@ def _check_is_permutation(indices, n_samples):
def permutation_test_score(estimator, X, y, *, groups=None, cv=None,
n_permutations=100, n_jobs=None, random_state=0,
verbose=0, scoring=None):
- """Evaluate the significance of a cross-validated score with permutations
+ """Evaluates the significance of a cross-validated score using permutations
- Read more in the :ref:`User Guide <cross_validation>`.
+ Permutes targets to generate 'randomized data' and compute the empirical
+ p-value against the null hypothesis that features and targets are
+ independent.
+
+ The p-value represents the fraction of randomized data sets where the
+ estimator performed as well or better than in the original data. A small
+ p-value suggests that there is a real dependency between features and
+ targets which has been used by the estimator to give good predictions.
+ A large p-value 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.
Parameters
----------
@@ -1129,10 +1139,10 @@ def permutation_test_score(estimator, X, y, *, groups=None, cv=None,
-----
This function implements Test 1 in:
- Ojala and Garriga. Permutation Tests for Studying Classifier
- Performance. The Journal of Machine Learning Research (2010)
- vol. 11
- `[pdf] <http://www.jmlr.org/papers/volume11/ojala10a/ojala10a.pdf>`_.
+ Ojala and Garriga. `Permutation Tests for Studying Classifier
+ Performance
+ <http://www.jmlr.org/papers/volume11/ojala10a/ojala10a.pdf>`_. The
+ Journal of Machine Learning Research (2010) vol. 11
"""
X, y, groups = indexable(X, y, groups)
