DOC fix typo in logistic regression
@@ -885,7 +885,7 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
and 'saga' are faster for large ones;
- For multiclass problems, only 'newton-cg', 'sag', 'saga' and
'lbfgs' handle multinomial loss;
- - 'liblinear' and is limited to one-versus-rest schemes.
+ - 'liblinear' is limited to one-versus-rest schemes.
- 'newton-cholesky' is a good choice for `n_samples` >> `n_features`,
especially with one-hot encoded categorical features with rare
categories. Note that it is limited to binary classification and the
