Constant Partying: Growing and Handling Trees with Constant Fits

This vignette describes infrastructure for regression and classification trees with simple constant fits in each of the terminal nodes. Thus, all observations that are predicted to be in the same terminal node also receive the same prediction, e.g., a mean for numeric responses or proportions for categorical responses. This class of trees is very common and includes all traditional tree variants (AID, CHAID, CART, C4.5, FACT, QUEST) and also more recent approaches like CTree. Trees inferred by any of these algorithms could in principle be represented by objects of class “constparty” in partykit that then provides unified methods for printing, plotting, and predicting. Here, we describe how one can create “constparty” objects by (a) coercion from other R classes, (b) parsing of XML descriptions of trees learned in other software systems, (c) learning a tree using one’s own algorithm.