From 81e65272387f690d787c3713e433a5508e77e23e Mon Sep 17 00:00:00 2001 From: jeremie du boisberranger Date: Wed, 27 Mar 2019 16:25:03 +0100 Subject: [PATCH] remove saga, add sparse_cg --- sklearn/linear_model/ridge.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/sklearn/linear_model/ridge.py b/sklearn/linear_model/ridge.py index d08be5a916c9a..e1fc9b42438e4 100644 --- a/sklearn/linear_model/ridge.py +++ b/sklearn/linear_model/ridge.py @@ -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