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[MRG] documentation for random_state in forest.py #15516

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Merged
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Nov 19, 2019
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Merged

[MRG] documentation for random_state in forest.py #15516

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ca334af
documentation for random_state in forests
MDouriez Nov 2, 2019
70d63d1
move note to parameter
MDouriez Nov 2, 2019
8ff9ed8
same for RandomForestRegressor
MDouriez Nov 2, 2019
48ed17a
add doc for ExtraTreesRegressor and ExtraTreesClassifier
MDouriez Nov 2, 2019
07204b3
skip line
Nov 18, 2019
07ae312
lint
Nov 18, 2019
3d810f9
move note back to where it was
MDouriez Nov 19, 2019
27e5b62
add Glossary in RandomForestRegressor
MDouriez Nov 19, 2019
c8fd235
adding description for RandomTreesEmbedding
MDouriez Nov 19, 2019
04b6ec3
Merge branch 'master' into 15222_forest
MDouriez Nov 19, 2019
bcdd5de
small fix
MDouriez Nov 19, 2019
5c8c098
correct description for RandomTreesEmbedding
MDouriez Nov 19, 2019
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53 changes: 31 additions & 22 deletions sklearn/ensemble/_forest.py
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Expand Up @@ -962,10 +962,11 @@ class RandomForestClassifier(ForestClassifier):
<n_jobs>` for more details.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``).
See :term:`Glossary <random_state>` for details.

verbose : int, optional (default=0)
Controls the verbosity when fitting and predicting.
Expand Down Expand Up @@ -1278,10 +1279,11 @@ class RandomForestRegressor(ForestRegressor):
<n_jobs>` for more details.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``).
See :term:`Glossary <random_state>` for details.

verbose : int, optional (default=0)
Controls the verbosity when fitting and predicting.
Expand Down Expand Up @@ -1540,7 +1542,7 @@ class ExtraTreesClassifier(ForestClassifier):

bootstrap : boolean, optional (default=False)
Whether bootstrap samples are used when building trees. If False, the
whole datset is used to build each tree.
whole dataset is used to build each tree.

oob_score : bool, optional (default=False)
Whether to use out-of-bag samples to estimate
Expand All @@ -1554,10 +1556,14 @@ class ExtraTreesClassifier(ForestClassifier):
<n_jobs>` for more details.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Controls 3 sources of randomness:

- the bootstrapping of the samples used when building trees
(if ``bootstrap=True``)
- the sampling of the features to consider when looking for the best
split at each node (if ``max_features < n_features``)
- the draw of the splits for each of the `max_features`
See :term:`Glossary <random_state>` for details.

verbose : int, optional (default=0)
Controls the verbosity when fitting and predicting.
Expand Down Expand Up @@ -1845,7 +1851,7 @@ class ExtraTreesRegressor(ForestRegressor):

bootstrap : boolean, optional (default=False)
Whether bootstrap samples are used when building trees. If False, the
whole datset is used to build each tree.
whole dataset is used to build each tree.

oob_score : bool, optional (default=False)
Whether to use out-of-bag samples to estimate the R^2 on unseen data.
Expand All @@ -1858,10 +1864,14 @@ class ExtraTreesRegressor(ForestRegressor):
<n_jobs>` for more details.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Controls 3 sources of randomness:

- the bootstrapping of the samples used when building trees
(if ``bootstrap=True``)
- the sampling of the features to consider when looking for the best
split at each node (if ``max_features < n_features``)
- the draw of the splits for each of the `max_features`
See :term:`Glossary <random_state>` for details.

verbose : int, optional (default=0)
Controls the verbosity when fitting and predicting.
Expand Down Expand Up @@ -2088,10 +2098,9 @@ class RandomTreesEmbedding(BaseForest):
<n_jobs>` for more details.

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Controls the generation of the random `y` used to fit the trees
and the draw of the splits for each feature at the trees' nodes.
See :term:`Glossary <random_state>` for details.

verbose : int, optional (default=0)
Controls the verbosity when fitting and predicting.
Expand Down