DOC improve random state docstring in ClassifierChain and RegressorChain
@@ -547,12 +547,13 @@ class ClassifierChain(MetaEstimatorMixin, ClassifierMixin, _BaseChain):
- An iterable yielding (train, test) splits as arrays of indices.
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`.
-
- The random number generator is used to generate random chain orders.
+ If ``order='random'``, determines random number generation for the
+ chain orders.
+ In addition, it controls the random seed given at each `base_estimator`
+ at each chaining iteration. Thus, it is only used when `base_estimator`
+ exposes a `random_state`.
+ Pass an int for reproducible output across multiple function calls.
+ See :term:`Glossary <random_state>`.
Attributes
----------
@@ -707,12 +708,13 @@ class RegressorChain(MetaEstimatorMixin, RegressorMixin, _BaseChain):
- An iterable yielding (train, test) splits as arrays of indices.
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`.
-
- The random number generator is used to generate random chain orders.
+ If ``order='random'``, determines random number generation for the
+ chain orders.
+ In addition, it controls the random seed given at each `base_estimator`
+ at each chaining iteration. Thus, it is only used when `base_estimator`
+ exposes a `random_state`.
+ Pass an int for reproducible output across multiple function calls.
+ See :term:`Glossary <random_state>`.
Attributes
----------
