Thanks to visit codestin.com
Credit goes to github.com

Skip to content

DOC fix deprecated log loss argument in user guide #24753

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 1 commit into from
Oct 25, 2022
Merged

DOC fix deprecated log loss argument in user guide #24753

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
2 changes: 1 addition & 1 deletion doc/glossary.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -284,7 +284,7 @@ General Concepts
>>> from sklearn.model_selection import GridSearchCV
>>> from sklearn.linear_model import SGDClassifier
>>> clf = GridSearchCV(SGDClassifier(),
... param_grid={'loss': ['log', 'hinge']})
... param_grid={'loss': ['log_loss', 'hinge']})

This means that we can only check for duck-typed attributes after
fitting, and that we must be careful to make :term:`meta-estimators`
Expand Down
8 changes: 4 additions & 4 deletions doc/modules/linear_model.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -126,9 +126,9 @@ its ``coef_`` member::
>>> reg.intercept_
0.13636...

Note that the class :class:`Ridge` allows for the user to specify that the
solver be automatically chosen by setting `solver="auto"`. When this option
is specified, :class:`Ridge` will choose between the `"lbfgs"`, `"cholesky"`,
Note that the class :class:`Ridge` allows for the user to specify that the
solver be automatically chosen by setting `solver="auto"`. When this option
is specified, :class:`Ridge` will choose between the `"lbfgs"`, `"cholesky"`,
and `"sparse_cg"` solvers. :class:`Ridge` will begin checking the conditions
shown in the following table from top to bottom. If the condition is true,
the corresponding solver is chosen.
Expand Down Expand Up @@ -1020,7 +1020,7 @@ The following table summarizes the penalties supported by each solver:
The "lbfgs" solver is used by default for its robustness. For large datasets
the "saga" solver is usually faster.
For large dataset, you may also consider using :class:`SGDClassifier`
with 'log' loss, which might be even faster but requires more tuning.
with `loss="log_loss"`, which might be even faster but requires more tuning.

.. topic:: Examples:

Expand Down