8000 [MRG+1] Improved docstring for permutation_test_score (#8379 and #8564) by leereeves · Pull Request #8569 · scikit-learn/scikit-learn · GitHub
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[MRG+1] Improved docstring for permutation_test_score (#8379 and #8564) #8569

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Mar 12, 2017
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[MRG+1] Improved docstring for permutation_test_score (#8379 and #8564) #8569

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b21724e
DOC improved explanation of p-value from permutation_test_score (#837…
leereeves Mar 10, 2017
a097a15
DOC add missing word 'that' in docstring
leereeves Mar 12, 2017
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13 changes: 8 additions & 5 deletions sklearn/cross_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1905,11 +1905,14 @@ def permutation_test_score(estimator, X, y, cv=None,
The scores obtained for each permutations.

pvalue : float
The returned value equals p-value if `scoring` returns bigger
numbers for better scores (e.g., accuracy_score). If `scoring` is
rather a loss function (i.e. when lower is better such as with
`mean_squared_error`) then this is actually the complement of the
p-value: 1 - p-value.
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
-----
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13 changes: 8 additions & 5 deletions sklearn/model_selection/_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -596,11 +596,14 @@ def permutation_test_score(estimator, X, y, groups=None, cv=None,
The scores obtained for each permutations.

pvalue : float
The returned value equals p-value if `scoring` returns bigger
numbers for better scores (e.g., accuracy_score). If `scoring` is
rather a loss function (i.e. when lower is better such as with
`mean_squared_error`) then this is actually the complement of the
p-value: 1 - p-value.
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
-----
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