Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Appearance settings

DOC Clarified n_jobs parallelization in plot_partial_dependence #19750

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.

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
Mar 23, 2021
Merged
Changes from all commits
Commits
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
3 changes: 3 additions & 0 deletions 3 sklearn/inspection/_plot/partial_dependence.py
Original file line number Diff line number Diff line change
Expand Up @@ -174,6 +174,9 @@ def plot_partial_dependence(
n_jobs : int, default=None
The number of CPUs to use to compute the partial dependences.
Computation is parallelized over features specified by the `features`
parameter.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
Expand Down
Morty Proxy This is a proxified and sanitized view of the page, visit original site.