Chunk #15 — The Methods — How Do Classification and Regression Trees Work?

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An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

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Classification and regression trees are a simple nonparametric regression approach. Their main characteristic is that the feature space, i.e. the space spanned by all predictor variables, is recursively partitioned into a set of rectangular areas, as illustrated below. The partition is created such that observations with similar response values are grouped. After the partition is completed, a constant value of the response variable is predicted within each area.