updated doc

docs/mllib-decision-tree.md

@@ -77,15 +77,17 @@ bins if the condition is not satisfied.
 
 **Categorical features**
 
-For `$M$` categorical features, one could come up with `$2^(M-1)-1$` split candidates. For
+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
 categorical feature values by the proportion of labels falling in one of the two classes (see
 Section 9.2.4 in
 [Elements of Statistical Machine Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for
 details). For example, for a binary classification problem with one categorical feature with three
 categories A, B and C with corresponding proportion of label 1 as 0.2, 0.6 and 0.4, the categorical
 features are ordered as A followed by C followed B or A, B, C. The two split candidates are A \| C, B
-and A , B \| C where \| denotes the split.
+and A , B \| C where \| denotes the split. A similar ordering using impurity is performed
+for categorical feature values in multiclass classification when `$2^(M-1)-1$` is
+greater than the number of bins.
 
 ### Stopping rule