Skip to content

DOC Ensures that Ridge passes numpydoc validation #20499

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Jul 10, 2021
Merged

DOC Ensures that Ridge passes numpydoc validation #20499

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
1dfab86
Remove Ridge from DOCSTRING_IGNORE_LIST
alinealfa Jul 10, 2021
67ea5de
Update docstring to pass validation PR09
alinealfa Jul 10, 2021
e4661e8
Update docstring to pass validation RT03
alinealfa Jul 10, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -129,7 +129,6 @@
"RandomTreesEmbedding",
"RandomizedSearchCV",
"RegressorChain",
"Ridge",
"RidgeCV",
"RidgeClassifier",
"RidgeClassifierCV",
Expand Down
7 changes: 4 additions & 3 deletions sklearn/linear_model/_ridge.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -988,18 +988,19 @@ def fit(self, X, y, sample_weight=None):
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
Training data
Training data.

y : ndarray of shape (n_samples,) or (n_samples, n_targets)
Target values
Target values.

sample_weight : float or ndarray of shape (n_samples,), default=None
Individual weights for each sample. If given a float, every sample
will have the same weight.

Returns
-------
self : returns an instance of self.
self : object
Fitted estimator.
"""
return super().fit(X, y, sample_weight=sample_weight)

Expand Down