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We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models.
UCI machine learning repository, 1987
Tjen-Sien Lim · 1987
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A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
Tjen-Sien Lim, Wei-Yin Loh, and Yu-Shan Shih · 2000
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Statistical modeling: The two cultures
Leo Breiman · 2001
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Classification and regression by randomforest
Andy Liaw and Matthew Wiener · 2002
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Random forest: A classification and regression tool for compound classification and qsar modeling
† Vladimir Svetnik, *, † Andy Liaw, † Christopher Tong, ‡ J. Christopher Culberson, § Robert P. Sheridan, , and Bradley P. Feuston‡ · 2003
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Random subwindows for robust image classification
Raphael Maree, Pierre Geurts, Justus Piater, and Louis Wehenkel · 2005
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Unbiased recursive partitioning: A conditional inference framework
Torsten Hothorn, Kurt Hornik, and Achim Zeileis · 2006
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Rotation forest: A new classifier ensemble method
Juan José Rodriguez, Ludmila I Kuncheva, and Carlos J Alonso · 2006
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Cost-sensitive multi-class classification from probability estimates
Deirdre B. O’Brien, Maya R. Gupta, and Robert M. Gray · 2008
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UCI machine learning repository, 2009
Paulo Cortez · 2009
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To explain or to predict?
Galit Shmueli · 2010
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Interpretation of qsar models based on random forest methods
Victor E. Kuz’min, Pavel G. Polishchuk, Anatoly G. Artemenko, and Sergey A. Andronati · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Using sensitivity analysis and visualization techniques to open black box data mining models
Paulo Cortez and Mark J. Embrechts · 2013
rfFC: Random Forest Feature Contributions
Richard Marchese Robinson Anna Palczewska · 2015
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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin · 2015
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R: A Language and Environment for Statistical Computing
R Core Team · 2015
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RStudio: Integrated Development Environment for R
RStudio Team · 2015
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Rborist: Extensible, Parallelizable Implementation of the Random Forest Algorithm
Mark Seligman · 2015
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In silico modelling of permeation enhancement potency in caco-2 monolayers based on molecular descriptors and random forest
Soeren H. Welling, Line K.H. Clemmensen, Stephen T. Buckley, Lars Hovgaard, Per B. Brockhoff, and Hanne H.F. Refsgaard · 2015
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Fast decorrelated neural network ensembles with random weights
Monther Alhamdoosh and Dianhui Wang · 2014
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Learning accurate and interpretable models based on regularized random forests regression
Sheng Liu, Shamitha Dissanayake, Sanjay Patel, Xin Dang, Todd Mlsna, Yixin Chen, and Dawn Wilkins · 2014
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Interpreting random forest classification models using a feature contribution method
Anna Palczewska, Jan Palczewski, Richard Marchese Robinson, and Daniel Neagu · 2014
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forestFloor: Visualizes Random Forests with Feature Contributions
Soeren Havelund Welling · 2015
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ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
M. N. Wright and A. Ziegler · 2015
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