Bias in random forest variable importance measures: Illustrations, sources and a solution
نویسندگان
چکیده
منابع مشابه
Random Forest variable importance with missing data
Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values. Possible solutions are given by imputation methods, complete case analysis and a newly suggested importance measure. However, it is unknown t...
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MOTIVATION We developed an EM-random forest (EMRF) for Haseman-Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of corr...
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Relative importance of regressor variables is an old topic that still awaits a satisfactory solution. When interest is in attributing importance in linear regression, averaging over orderings methods for decomposing R2 are among the state-of-theart methods, although the mechanism behind their behavior is not (yet) completely understood. Random forests—a machinelearning tool for classification a...
متن کاملLetter to the Editor: Stability of Random Forest importance measures
The goal of this article (letter to the editor) is to emphasize the value of exploring ranking stability when using the importance measures, mean decrease accuracy (MDA) and mean decrease Gini (MDG), provided by Random Forest. We illustrate with a real and a simulated example that ranks based on the MDA are unstable to small perturbations of the dataset and ranks based on the MDG provide more r...
متن کاملLetter to the Editor: On the stability and ranking of predictors from random forest variable importance measures
A recent study examined the stability of rankings from random forests using two variable importance measures (mean decrease accuracy (MDA) and mean decrease Gini (MDG)) and concluded that rankings based on the MDG were more robust than MDA. However, studies examining data-specific characteristics on ranking stability have been few. Rankings based on the MDG measure showed sensitivity to within-...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2007
ISSN: 1471-2105
DOI: 10.1186/1471-2105-8-25