Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformaticsAnne‐Laure Boulesteix, Inke R. König, Jochen Kruppa et al.|Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery|2012Cited by 937
An AUC-based permutation variable importance measure for random forestsSilke Janitza, Anne‐Laure Boulesteix, Carolin Strobl|BMC Bioinformatics|2013Cited by 264
Random forest for ordinal responses: Prediction and variable selectionSilke Janitza, Anne‐Laure Boulesteix, Gerhard Tutz|Computational Statistics & Data Analysis|2015Cited by 229
A computationally fast variable importance test for random forests for high-dimensional dataSilke Janitza, Anne‐Laure Boulesteix, Ender Celik|Advances in Data Analysis and Classification|2016Cited by 204
Subsampling Versus Bootstrapping in Resampling-Based Model Selection for Multivariable RegressionRiccardo De Bin, Anne‐Laure Boulesteix, Willi Sauerbrei et al.|Biometrics|2015Cited by 107