Psychologisches Kolloquium

Prof. Dr. Carolin Strobl (Universität Zürich):

A Statistician’s Botanical Garden - The Ideas behind Trees, Model-Based Trees and Random Forests

Abstract

Classification and regression trees, model-based trees and random forests are powerful statistical methods from the field of machine learning. They have been shown to achieve a high prediction accuracy, especially in big data applications with many predictor variables and complex association patterns (such as nonlinear and higher-order interaction effects). While individual trees are easy to interpret, random forests are "black box" prediction methods. They do, however, provide variable importance measures, that are being used to judge the relevance of the individual predictor variables. The aim of this presentation is to introduce the rationale behind trees, model-based trees and random forests, to illustrate their potential for high-dimensional data exploration in psychological research, but also to point out limitations and potential pitfalls in their practical application.

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