Recursive partition (RP) models are a flexible method for specifying the conditional distribution of a variable y, given a vector of predictor values x. Such models use a tree structure to recursively partition the predictor space into subsets where the distribution of y is successively more homogeneous. The terminal nodes of the tree correspond to the distinct regions of the partition, and the partition is determined by splitting rules associated with each of the internal nodes. By moving from the root node through to the terminal node of the tree, each observation is then assigned to a unique terminal node where the conditional distribution of y is determined. The two most common response types are continuous and categorical, with corresponding tasks often known as regression and classification.