DOC Updates random_state descriptions for sklearn/preprocessing/_data.py
@@ -2176,11 +2176,11 @@ class QuantileTransformer(TransformerMixin, BaseEstimator):
differ for value-identical sparse and dense matrices.
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. Note that this is used by subsampling and smoothing
+ Determines random number generation for subsampling and smoothing
noise.
+ Please see ``subsample`` for more details.
+ Pass an int for reproducible results across multiple function calls.
+ See :term:`Glossary <random_state>`
copy : boolean, optional, (default=True)
Set to False to perform inplace transformation and avoid a copy (if the
@@ -2605,11 +2605,11 @@ def quantile_transform(X, axis=0, n_quantiles=1000,
differ for value-identical sparse and dense matrices.
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. Note that this is used by subsampling and smoothing
+ Determines random number generation for subsampling and smoothing
noise.
+ Please see ``subsample`` for more details.
+ Pass an int for reproducible results across multiple function calls.
+ See :term:`Glossary <random_state>`
copy : boolean, optional, (default=True)
Set to False to perform inplace transformation and avoid a copy (if the
