DOC make more obvious that logistic regression is regularized by default
@@ -735,7 +735,14 @@ of a single trial are modeled using a
Logistic regression is implemented in :class:`LogisticRegression`.
This implementation can fit binary, One-vs-Rest, or multinomial logistic
regression with optional :math:`\ell_1`, :math:`\ell_2` or Elastic-Net
-regularization. Note that regularization is applied by default.
+regularization.
+
+.. note::
+
+ Regularization is applied by default, which is common in machine
+ learning but not in statistics. Another advantage of regularization is
+ that it improves numerical stability. No regularization amounts to
+ setting C to a very high value.
As an optimization problem, binary class :math:`\ell_2` penalized logistic
regression minimizes the following cost function:
