DOC gradient boosting fit() supports sparse X
@@ -1358,9 +1358,10 @@ class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble)):
Parameters
----------
- X : array-like, shape (n_samples, n_features)
- Training vectors, where n_samples is the number of samples
- and n_features is the number of features.
+ X : {array-like, sparse matrix}, shape (n_samples, n_features)
+ The input samples. Internally, it will be converted to
+ ``dtype=np.float32`` and if a sparse matrix is provided
+ to a sparse ``csr_matrix``.
y : array-like, shape (n_samples,)
Target values (strings or integers in classification, real numbers
