Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/2063#discussion_r16511789
--- Diff: docs/mllib-decision-tree.md ---
@@ -77,109 +85,316 @@ bins if the condition is not satisfied.
**Categorical features**
-For `$M$` categorical feature values, one could come up with `$2^(M-1)-1$`
split candidates. For
-binary classification, we can reduce the number of split candidates to
`$M-1$` by ordering the
+For a categorical feature with `$M$` possible values (categories), one
could come up with
+`$2^{M-1}-1$` split candidates. For binary classification and regression,
+we can reduce the number of split candidates to `$M-1$` by ordering the
categorical feature values by the proportion of labels falling in one of
the two classes (see
--- End diff --
Correct.
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