Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31113
Authors: 
Strobl, Carolin
Year of Publication: 
2005
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 420
Abstract: 
Evidence for variable selection bias in classification tree algorithms based on the Gini Index is reviewed from the literature and embedded into a broader explanatory scheme: Variable selection bias in classification tree algorithms based on the Gini Index can be caused not only by the statistical effect of multiple comparisons, but also by an increasing estimation bias and variance of the splitting criterion when plug-in estimates of entropy measures like the Gini Index are employed. The relevance of these sources of variable selection bias in the different simulation study designs is examined. Variable selection bias due to the explored sources applies to all classification tree algorithms based on empirical entropy measures like the Gini Index, Deviance and Information Gain, and to both binary and multiway splitting algorithms.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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