Apply the decision tree method to regression problems. It is quite similar to regression trees except that a qualitative, not quantitive, response is needed. This leads to following differences:

• The criterion for making the binary splits is no longer RSS.
• The most commonly class in the region, rather than the mean value, is used as the prediction result. Classification Tree is used to predict qualitative variables instead of quantitative variables. It is very similar to regression tree and use recursive binary splitting to generate the classification tree. But use Gini index $G = \sum_{k=1}^{K} \hat{p}_{mk} (1-\hat{p}_{mk})$ and entropy as the indicator. $D = -\sum_{k=1}^{K} \hat{p}_{mk} log \hat{p}_{mk}$

The small value of purity of node indicated that a node contains observations from almost the same category. Entropy is the same as Gini Index, if the $m$th node is pure, the entropy is very small.