DOC Improve random_state descriptions for GradientBoosting
@@ -885,10 +885,14 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
``DummyEstimator`` predicting the classes priors is used.
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 random seed given to each Tree estimator at each
+ boosting iteration.
+ In addition, it controls the random permutation of the features at
+ each split (see Notes for more details).
+ It also controls the random spliting of the training data to obtain a
+ validation set if `n_iter_no_change` is not None.
+ Pass an int for reproducible output across multiple function calls.
+ See :term:`Glossary <random_state>`.
max_features : int, float, string or None, optional (default=None)
The number of features to consider when looking for the best split:
@@ -1358,10 +1362,14 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
(for loss='ls'), or a quantile for the other losses.
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 random seed given to each Tree estimator at each
+ boosting iteration.
+ In addition, it controls the random permutation of the features at
+ each split (see Notes for more details).
+ It also controls the random spliting of the training data to obtain a
+ validation set if `n_iter_no_change` is not None.
+ Pass an int for reproducible output across multiple function calls.
+ See :term:`Glossary <random_state>`.
max_features : int, float, string or None, optional (default=None)
The number of features to consider when looking for the best split:
