From 2d9f52068481cd0a8e85897e6770a7d763d623eb Mon Sep 17 00:00:00 2001 From: diegodlh Date: Thu, 10 Aug 2017 13:39:34 -0300 Subject: [PATCH] Fixed impossible min_samples_split value --- doc/modules/ensemble.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index b766f4dfd4d0c..56bddcd172d95 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -202,7 +202,7 @@ bias. Empirical good default values are ``max_features=n_features`` for regression problems, and ``max_features=sqrt(n_features)`` for classification tasks (where ``n_features`` is the number of features in the data). Good results are often achieved when setting ``max_depth=None`` -in combination with ``min_samples_split=1`` (i.e., when fully developing the +in combination with ``min_samples_split=2`` (i.e., when fully developing the trees). Bear in mind though that these values are usually not optimal, and might result in models that consume a lot of RAM. The best parameter values should always be cross-validated. In addition, note that in random forests,