updated doc

docs/mllib-decision-tree.md

@@ -85,9 +85,9 @@ Section 9.2.4 in
 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. 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.
+and A , B \| C where \| denotes the split. A similar heuristic is used for multiclass classification
+when `$2^(M-1)-1$` is greater than the number of bins -- the impurity for each categorical feature value
+is used for ordering.
 
 ### Stopping rule