Skip to content

DOC add additional pointers in Forest regarding how to use warm_start #24579

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 8 commits into from
Nov 13, 2022
Merged

DOC add additional pointers in Forest regarding how to use warm_start #24579

Changes from all commits
Commits
Show all changes
8 commits
Select commit Hold shift + click to select a range
3b44a0b
Edited docs for warm_start and removed OOB computation for n_estimato…
Oct 4, 2022
ff9ddb4
Merge branch 'main' into main
Oct 6, 2022
d1d7813
Merge branch 'main' of github.com:scikit-learn/scikit-learn into main
Oct 8, 2022
b2c2877
Merge branch 'main' into main
Oct 9, 2022
7641002
Merge branch 'main' of github.com:scikit-learn/scikit-learn into main
Nov 11, 2022
90eaaca
Merge remote-tracking branch 'upstream/main' into main
Nov 12, 2022
10cc943
Removed change to OOB computation
Nov 12, 2022
392af51
Merge branch 'main' of github.com:choo8/scikit-learn into main
Nov 12, 2022
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
15 changes: 10 additions & 5 deletions sklearn/ensemble/_forest.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1217,7 +1217,8 @@ class RandomForestClassifier(ForestClassifier):
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
new forest. See :term:`Glossary <warm_start>` and
:ref:`gradient_boosting_warm_start` for details.

class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
default=None
Expand Down Expand Up @@ -1593,7 +1594,8 @@ class RandomForestRegressor(ForestRegressor):
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
new forest. See :term:`Glossary <warm_start>` and
:ref:`gradient_boosting_warm_start` for details.

ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
Expand Down Expand Up @@ -1914,7 +1916,8 @@ class ExtraTreesClassifier(ForestClassifier):
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
new forest. See :term:`Glossary <warm_start>` and
:ref:`gradient_boosting_warm_start` for details.

class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
default=None
Expand Down Expand Up @@ -2281,7 +2284,8 @@ class ExtraTreesRegressor(ForestRegressor):
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
new forest. See :term:`Glossary <warm_start>` and
:ref:`gradient_boosting_warm_start` for details.

ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
Expand Down Expand Up @@ -2556,7 +2560,8 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest):
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
new forest. See :term:`Glossary <warm_start>` and
:ref:`gradient_boosting_warm_start` for details.

Attributes
----------
Expand Down