DOC Ridge fit_intercept sparse support: remove saga, add sparse_cg
@@ -290,7 +290,8 @@ def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
All last five solvers support both dense and sparse data. However, only
- 'sag' and 'saga' supports sparse input when`fit_intercept` is True.
+ 'sag' and 'sparse_cg' supports sparse input when`fit_intercept` is
+ True.
.. versionadded:: 0.17
Stochastic Average Gradient descent solver.
@@ -651,8 +652,8 @@ class Ridge(_BaseRidge, RegressorMixin):
approximately the same scale. You can preprocess the data with a
scaler from sklearn.preprocessing.
- All last five solvers support both dense and sparse data. However,
- only 'sag' and 'saga' supports sparse input when `fit_intercept` is
+ All last five solvers support both dense and sparse data. However, only
+ 'sag' and 'sparse_cg' supports sparse input when `fit_intercept` is
True.
.. versionadded:: 0.17
