Evaluating Model-based Trees in Practice

Zeileis, Achim and Hothorn, Torsten and Hornik, Kurt ORCID: https://orcid.org/0000-0003-4198-9911 (2006) Evaluating Model-based Trees in Practice. Research Report Series / Department of Statistics and Mathematics, 32. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.


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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.

Item Type: Paper
Keywords: benchmark study / recursive partitioning / prediction / complexity
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 12 Apr 2006 16:46
Last Modified: 24 Oct 2019 13:41
URI: https://epub.wu.ac.at/id/eprint/1484


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