article Open AccessTop 1% cited
Conditional variable importance for random forests
BMC Bioinformatics · 2008 · Vol. 9(1) · pp. 307–307
Carolin Strobl✉(Ludwig-Maximilians-Universität München)Anne‐Laure Boulesteix(Sylvia Lawry Centre for Multiple Sclerosis Research)Thomas Kneib(Ludwig-Maximilians-Universität München)Thomas Augustin(Ludwig-Maximilians-Universität München)Achim Zeileis(Vienna University of Economics and Business)
Abstract
The resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach.
Gene expression and cancer classificationBioinformatics and Genomic NetworksStatistical Methods and InferenceVariable (mathematics)Permutation (music)Feature selectionRandom forestTree (set theory)VariablesComputationRandom variableMeasure (data warehouse)Computer science
MeSH terms
Amino Acid SequenceBinding SitesBiometryDecision TreesFactor Analysis, StatisticalMajor Histocompatibility ComplexRegression AnalysisResearch DesignStatistics, NonparametricComputational Biology
Funding
- Porticus Foundation
Citations
3,188
FWCI
15.45
field-weighted impact
References
39
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99%
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