8000 DOC Ensures that SelectFdr passes numpydoc validation by jmloyola · Pull Request #20984 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that SelectFdr passes numpydoc validation #20984

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Merged
merged 2 commits into from
Sep 9, 2021
Merged

DOC Ensures that SelectFdr passes numpydoc validation #20984

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bcf1893
Remove SelectFdr from DOCSTRING_IGNORE_LIST
jmloyola Sep 8, 2021
98db883
Fix numpydocs from SelectFdr
jmloyola Sep 8, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,6 @@
"RobustScaler",
"SGDOneClassSVM",
"SGDRegressor",
"SelectFdr",
"SelectFpr",
"SelectFromModel",
"SelectFwe",
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32 changes: 16 additions & 16 deletions sklearn/feature_selection/_univariate_selection.py
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Expand Up @@ -699,7 +699,7 @@ def _get_support_mask(self):


class SelectFdr(_BaseFilter):
"""Filter: Select the p-values for an estimated false discovery rate
"""Filter: Select the p-values for an estimated false discovery rate.

This uses the Benjamini-Hochberg procedure. ``alpha`` is an upper bound
on the expected false discovery rate.
Expand All @@ -717,17 +717,6 @@ class SelectFdr(_BaseFilter):
alpha : float, default=5e-2
The highest uncorrected p-value for features to keep.

Examples
--------
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import SelectFdr, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> X_new = SelectFdr(chi2, alpha=0.01).fit_transform(X, y)
>>> X_new.shape
(569, 16)

Attributes
----------
scores_ : array-like of shape (n_features,)
Expand All @@ -747,10 +736,6 @@ class SelectFdr(_BaseFilter):

.. versionadded:: 1.0

References
----------
https://en.wikipedia.org/wiki/False_discovery_rate

See Also
--------
f_classif : ANOVA F-value between label/feature for classification tasks.
Expand All @@ -765,6 +750,21 @@ class SelectFdr(_BaseFilter):
SelectFwe : Select features based on family-wise error rate.
GenericUnivariateSelect : Univariate feature selector with configurable
mode.

References
----------
https://en.wikipedia.org/wiki/False_discovery_rate

Examples
--------
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import SelectFdr, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> X_new = SelectFdr(chi2, alpha=0.01).fit_transform(X, y)
>>> X_new.shape
(569, 16)
"""

def __init__(self, score_func=f_classif, *, alpha=5e-2):
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0