Skip to content

DOC set max_features for HGBT as MajorFeature #27892

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 3, 2023
Merged

DOC set max_features for HGBT as MajorFeature #27892

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
14 changes: 7 additions & 7 deletions doc/whats_new/v1.4.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -310,7 +310,7 @@ Changelog
- |Fix| :func:`decomposition.dict_learning_online` does not ignore anymore the parameter
`max_iter`.
:pr:`27834` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| The `degree` parameter in the :class:`decomposition.KernelPCA`
constructor now accepts real values instead of only integral values in
accordance with the `degree` parameter of the
Expand Down Expand Up @@ -339,6 +339,12 @@ Changelog
dtype as categories in the algorithm. `categorical_features="from_dtype"` will become
the default in v1.6. :pr:`26411` by `Thomas Fan`_.

- |MajorFeature| :class:`ensemble.HistGradientBoostingClassifier` and
:class:`ensemble.HistGradientBoostingRegressor` got the new parameter
`max_features` to specify the proportion of randomly chosen features considered
in each split.
:pr:`27139` by :user:`Christian Lorentzen <lorentzenchr>`.

- |Feature| :class:`ensemble.RandomForestClassifier`,
:class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`
and :class:`ensemble.ExtraTreesRegressor` now support monotonic constraints,
Expand All @@ -347,12 +353,6 @@ Changelog
:pr:`13649` by :user:`Samuel Ronsin <samronsin>`,
initiated by :user:`Patrick O'Reilly <pat-oreilly>`.

- |Feature| :class:`ensemble.HistGradientBoostingClassifier` and
:class:`ensemble.HistGradientBoostingRegressor` got the new parameter
`max_features` to specify the proportion of randomly chosen features considered
in each split.
:pr:`27139` by :user:`Christian Lorentzen <lorentzenchr>`.

- |Efficiency| :class:`ensemble.GradientBoostingClassifier` is faster,
for binary and in particular for multiclass problems thanks to the private loss
function module.
Expand Down