8000 DOC Ensures that SelectFpr passes numpydoc validation by jmloyola · Pull Request #20985 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that SelectFpr passes numpydoc validation #20985

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Merged
merged 3 commits into from
Sep 9, 2021
Merged

DOC Ensures that SelectFpr passes numpydoc validation #20985

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78c2ac1
Remove SelectFpr from DOCSTRING_IGNORE_LIST
jmloyola Sep 8, 2021
f0bce2e
Fix numpydocs from SelectFpr
jmloyola Sep 8, 2021
f0acd02
Merge branch 'main' into fix_numpydoc_selectfpr
glemaitre Sep 9, 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 @@ -60,7 +60,6 @@
"RobustScaler",
"SGDOneClassSVM",
"SGDRegressor",
"SelectFpr",
"SelectFromModel",
"SelectFwe",
"SelfTrainingClassifier",
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22 changes: 11 additions & 11 deletions sklearn/feature_selection/_univariate_selection.py
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Expand Up @@ -661,17 +661,6 @@ class SelectFpr(_BaseFilter):

.. versionadded:: 1.0

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

See Also
--------
f_classif : ANOVA F-value between label/feature for classification tasks.
Expand All @@ -686,6 +675,17 @@ class SelectFpr(_BaseFilter):
SelectFwe : Select features based on family-wise error rate.
GenericUnivariateSelect : Univariate feature selector with configurable
mode.

Examples
--------
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import SelectFpr, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> X_new = SelectFpr(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