DOC add additional pointers in Forest regarding how to use `warm_start`
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
----------
