8000 Explain solver choices for LogisticRegression by amueller · Pull Request #12768 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

Explain solver choices for LogisticRegression #12768

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
Dec 14, 2018
Merged

Explain solver choices for LogisticRegression #12768

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
7 changes: 3 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 @@ -817,11 +817,10 @@ The following table summarizes the penalties supported by each solver:
| Robust to unscaled datasets | yes | yes | yes | no | no |
+------------------------------+-----------------+-------------+-----------------+-----------+------------+

The "saga" solver is often the best choice but requires scaling. The
"lbfgs" solver is used by default for historical reasons.

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.
with 'log' loss, which might be even faster but require more tuning.

.. topic:: Examples:

Expand Down
0