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Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications.
Classification and regression trees
Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone · 1984
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Random Forests
Leo Breiman · 2001
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An extensive comparison of recent classification tools applied to microarray data
Jung Bok Jae Won Lee, Jung Bok Jae Won Lee, Mira Park, and Seuck Heun Song · 2005
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Gene Selection and Classification of Microarray Data Using Random Forest
R Diaz-Uriarte and S de Andrés · 2006
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Unbiased Recursive Partitioning: A Conditional Inference Framework
T Hothorn, K Hornik, and A Zeileis · 2006
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Unbiased Split Selection for Classification Trees Based on the Gini Index
C Strobl, A L Boulesteix, and T Augustin · 2007
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Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution
Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn · 2007
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A Framework to Identify Physiological Responses in Microarray Based Gene Expression Studies: Selection and Interpretation of Biologically Relevant Genes
Wendy Rodenburg, A. Geert Heidema, M. A. Jolanda Boer, I. M. Ingeborg Bovee-Oudenhoven, J. M. Edith Feskens, C. M. Edwin Mariman, and Jaap Keijer · 2008
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A bias correction algorithm for the gini variable importance measure in classification trees
Marco Sandri and Paola Zuccolotto · 2008
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Conditional variable importance for random forests
Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis · 2008
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Party on! A New, Conditional Variable-Importance Measure for Random Forests Available in the party Package
Strobl Carolin, Hothorn Torsten, and Zeileis Achim · 2009
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Unlocking the secrets of the genome
Susan E Celniker, Laura AL Dillon, Mark B Gerstein, Kristin C Gunsalus, Steven Henikoff, Gary H Karpen, Manolis Kellis, Eric C Lai, Jason D Lieb, David M MacAlpine, et al · 2009
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Developmental roles of 21 drosophila transcription factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions
Stewart MacArthur, Xiao-Yong Li, Jingyi Li, James B Brown, Hou Cheng Chu, Lucy Zeng, Brandi P Grondona, Aaron Hechmer, Lisa Simirenko, and Soile VE Keränen · 2009
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Inferring regulatory networks from expression data using tree-based methods
Vân Anh Huynh-Thu, Alexandre Irrthum, Louis Wehenkel, and Pierre Geurts · 2010
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Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, O. Grisel, M. Blondel, B. Prettenhofer, R. Weiss, and V. Dubourg · 2011
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Understanding variable importances in forests of randomized trees
Gilles Louppe, Louis Wehenkel, Antonio Sutera, and Pierre Geurts · 2013
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Fifty years of classification and regression trees
Wei-Yin Loh · 2014
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A computationally fast variable importance test for random forests for high-dimensional data
Silke Janitza, Ender Celik, and Anne Laure Boulesteix · 2016
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“Why Should I Trust You?” Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Variable importance using decision trees
Jalil Kazemitabar, Arash Amini, Adam Bloniarz, and Ameet S Talwalkar · 2017
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ranger: A fast implementation of random forests for high dimensional data in c++ and r
Marvin Wright and Andreas Ziegler · 2017
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Iterative random forests to discover predictive and stable high-order interactions
Sumanta Basu, Karl Kumbier, James B. Brown, and Bin Yu · 2018
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Refining interaction search through signed iterative random forests
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Gilles Louppe · 2014
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Interpreting random forests, 2014
Ando Saabas · 2014
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Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko · 2014
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Consistency of random forests
Erwan Scornet, Gerard Biau, and Jean Philippe Vert · 2015
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Variable importance analysis: A comprehensive review
Pengfei Wei, Zhenzhou Lu, and Jingwen Song · 2015
Cited alongside, same era.
XGBoost: A Scalable Tree Boosting System
Tianqi Chen and Carlos Guestrin · 2016
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Karl Kumbier, Sumanta Basu, James B Brown, Susan Celniker, and Bin Yu · 2018
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Consistent Individualized Feature Attribution for Tree Ensembles
Scott M. Lundberg, Gabriel G. Erion, and Su-In Lee · 2018
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The revival of the Gini importance?
Stefano Nembrini, Inke R. König, and Marvin N. Wright · 2018
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager and Susan Athey · 2018
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Interpretable machine learning: definitions, methods, and applications
W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
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Unbiased measurement of feature importance in tree-based methods
Zhengze Zhou and Giles Hooker · 2019
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