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Tree ensemble methods such as random forests [Breiman, 2001] are very popular to handle high-dimensional tabular data sets, notably because of their good predictive accuracy.
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C.J. Stone · 1985
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Random forests
L. Breiman · 2001
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A. Liaw and M. Wiener · 2002
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Bias in random forest variable importance measures: Illustrations, sources and a solution
C. Strobl, A.-L. Boulesteix, A. Zeileis, and T. Hothorn · 2007
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R. Genuer, J.-M. Poggi, and C. Tuleau · 2008
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Randomized trees for human pose detection
G. Rogez, J. Rihan, S. Ramalingam, C. Orrite, and P. H. Torr · 2008
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Danger: High power!–exploring the statistical properties of a test for random forest variable importance
C. Strobl and A. Zeileis · 2008
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C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis · 2008
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Predictor correlation impacts machine learning algorithms: implications for genomic studies
K.K. Nicodemus and J. D. Malley · 2009
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C. Strobl, T. Hothorn, and A. Zeileis · 2009
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R. Genuer, J.-M. Poggi, and C. Tuleau-Malot · 2010
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Random forest gini importance favours snps with large minor allele frequency: impact, sources and recommendations
A.-L. Boulesteix, A. Bender, J. Lorenzo Bermejo, and C. Strobl · 2011
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W.-Y. Loh · 2011
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K.K. Nicodemus · 2011
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Analysis of a random forests model
G. Biau · 2012
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Interpreting multiple linear regression: A guidebook of variable importance
L.L. Nathans, F.L. Oswald, and K. Nimon · 2012
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Ensemble Machine Learning , chapter Random forest for bioinformatics, pages 307–323
Y. Qi · 2012
Adaptive concentration of regression trees, with application to random forests
S. Wager and G. Walther · 2015
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Linear models
S.R. Searle and M.H.J. Gruber · 2016
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Context-dependent feature analysis with random forests
A. Sutera, G. Louppe, V.A. Huynh-Thu, L. Wehenkel, and P. Geurts · 2016
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Do little interactions get lost in dark random forests?
M.N. Wright, A. Ziegler, and I.R. König · 2016
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B. Gregorutti, B. Michel, and P. Saint-Pierre · 2017
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T.J. Hastie and R.J. Tibshirani · 2017
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R: A Language and Environment for Statistical Computing
R Core Team · 2013
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Analysis of purely random forests bias
S. Arlot and R. Genuer · 2014
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Do we need hundreds of classifiers to solve real world classification problems?
M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim · 2014
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The R Package optimization: Flexible Global Optimization with Simulated-Annealing , 2017
K. Husmann, A. Lange, and E. Spiegel · 2017
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J.M. Klusowski · 2019
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A debiased mdi feature importance measure for random forests
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Classification tree algorithm for grouped variables
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Asymptotic unbiasedness of the permutation importance measure in random forest models
B. Ramosaj and M. Pauly · 2019
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MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
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