Benchmarking Open-Source Tree Learners in R/RWeka

Schauerhuber, Michael and Zeileis, Achim and Meyer, David and Hornik, Kurt ORCID: https://orcid.org/0000-0003-4198-9911 (2007) Benchmarking Open-Source Tree Learners in R/RWeka. Research Report Series / Department of Statistics and Mathematics, 54. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.

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Abstract

The two most popular classification tree algorithms in machine learning and statistics - C4.5 and CART - are compared in a benchmark experiment together with two other more recent constant-fit tree learners from the statistics literature (QUEST, conditional inference trees). The study assesses both misclassification error and model complexity on bootstrap replications of 18 different benchmark datasets. It is carried out in the R system for statistical computing, made possible by means of the RWeka package which interfaces R to the open-source machine learning toolbox Weka. Both algorithms are found to be competitive in terms of misclassification error - with the performance difference clearly varying across data sets. However, C4.5 tends to grow larger and thus more complex trees. (author's abstract)

Item Type: Paper
Keywords: decision trees / benchmark experiment / R / Weka / open-source software
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 22 May 2007 16:39
Last Modified: 24 Oct 2019 13:41
URI: https://epub.wu.ac.at/id/eprint/1496

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