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Diffstat (limited to 'docs/mllib-decision-tree.md')
| -rw-r--r-- | docs/mllib-decision-tree.md | 8 |
1 files changed, 5 insertions, 3 deletions
diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md index 9cd768599e..9cbd880897 100644 --- a/docs/mllib-decision-tree.md +++ b/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 `$2^M-1$` split candidates. However, for -binary classification, the number of split candidates can be reduced to `$M-1$` by ordering the +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 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 |