Evaluating Model-based Trees in Practice

TL;DR: A recently suggested algorithm for recursive partitioning of statistical models, such as models estimated by maximum likelihood or least squares, is evaluated in practice, showing that the algorithm yields interpretable trees, competitive with previously suggested approaches.

Abstract: A recently suggested algorithm for recursive partitioning of statistical models (Zeileis, Hothorn and Hornik, 2005), such as models estimated by maximum likelihood or least squares, is evaluated in practice. The general algorithm is applied to linear regression, logisitic regression and survival regression and applied to economical and medical regression problems. Furthermore, its performance with respect to prediction quality and model complexity is compared in a benchmark study with a large collection of other tree-based algorithms showing that the algorithm yields interpretable trees, competitive with previously suggested approaches.