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DOC Ensures that LogisticRegressionCV passes numpydoc validation #20376

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Merged
merged 5 commits into from
Jun 28, 2021
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DOC Ensures that LogisticRegressionCV passes numpydoc validation #20376

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01f3944
Adresses #20308 LogisticRegressionCV
gitdoluquita Jun 26, 2021
9961120
Merge branch 'main' of scikit-learn
gitdoluquita Jun 26, 2021
a6700ba
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
gitdoluquita Jun 27, 2021
453e8fa
Fixing suggestions
gitdoluquita Jun 27, 2021
dd2ce82
Update sklearn/linear_model/_logistic.py
glemaitre Jun 28, 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 @@ -100,7 +100,6 @@
"LinearSVR",
"LocalOutlierFactor",
"LocallyLinearEmbedding",
"LogisticRegressionCV",
"MDS",
"MLPClassifier",
"MLPRegressor",
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15 changes: 7 additions & 8 deletions sklearn/linear_model/_logistic.py
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Expand Up @@ -1938,6 +1938,11 @@ class LogisticRegressionCV(LogisticRegression, LinearClassifierMixin, BaseEstima

.. versionadded:: 0.24

See Also
--------
LogisticRegression : Logistic regression without tuning the
hyperparameter `C`.

Examples
--------
>>> from sklearn.datasets import load_iris
Expand All @@ -1950,11 +1955,6 @@ class LogisticRegressionCV(LogisticRegression, LinearClassifierMixin, BaseEstima
(2, 3)
>>> clf.score(X, y)
0.98...

See Also
--------
LogisticRegression

"""

def __init__(
Expand Down Expand Up @@ -2015,6 +2015,7 @@ def fit(self, X, y, sample_weight=None):
Returns
-------
self : object
Fitted LogisticRegressionCV estimator.
"""
solver = _check_solver(self.solver, self.penalty, self.dual)

Expand Down Expand Up @@ -2346,8 +2347,7 @@ def fit(self, X, y, sample_weight=None):
return self

def score(self, X, y, sample_weight=None):
"""Returns the score using the `scoring` option on the given
test data and labels.
"""Score using the `scoring` option on the given test data and labels.

Parameters
----------
Expand All @@ -2364,7 +2364,6 @@ def score(self, X, y, sample_weight=None):
-------
score : float
Score of self.predict(X) wrt. y.

"""
scoring = self.scoring or "accuracy"
scoring = get_scorer(scoring)
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