OPTIMAL PARTITIONING FOR CLASSIFICATION AND REGRESSION TREES

In designing a decision tree for classification or regression, one selects at each node a feature to be tested, and partitions the range of that feature into a small number of bins, each bin corresponding to a child of the node. When the feature's range is discrete with N unordered outcomes, the optimal partition, that is, the partition minimizing an expected loss, is usually found by an exhaustive search through all possible partitions. Since the number of possible partitions grows exponentially in N, this approach is impractical when N is larger than about 10 or 20. In this paper, we present an iterative algorithm that finds a locally optimal partition for an arbitrary loss function, in time linear in N for each iteration. The algorithm is a K-means like clustering algorithm that uses as its distance measure a generalization of Kullback's information divergence. Moreover, we prove that the globally optimal partition must satisfy a nearest neighbor condition using divergence as the distance measure. These results generalize similar results of Breiman et al. to an arbitrary number of classes or regression variables and to an arbitrary number of bins. We also provide experimental results on a text-to-speech example, and we suggest additional applications of the algorithm, including the design of variable combinations, surrogate splits, composite nodes, and decision graphs.