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Random forests remain among the most popular off-the-shelf supervised machine learning tools with a well-established track record of predictive accuracy in both regression and classification settings.
Analyzing cart
Jason M. Klusowski · 1906
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Measuring the algorithmic convergence of randomized ensembles: The regression setting
Miles E Lopes, Suofei Wu, and Thomas Lee · 1908
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Interrelation of the regression models used for structure-activity analyses
Arthur Cammarata · 1972
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Hedonic housing prices and the demand for clean air
David Harrison Jr and Daniel L Rubinfeld · 1978
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Classification and Regression Trees
Leo Breiman, Jerome Friedman, Charles J. Stone, and R.A. Olshen · 1984
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How biased is the apparent error rate of a prediction rule?
Bradley Efron · 1986
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Attributes of the performance of central processing units: A relative performance prediction model
Phillip Ein-Dor and Jacob Feldmesser · 1987
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Generalized Additive Models
Bradley Efron and Robert Tibshirani · 1990
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Multivariate Adaptive Regression Splines
Jerome H Friedman · 1991
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Combining instance-based and model-based learning
J Ross Quinlan · 1993
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Weighted holistic invariant molecular descriptors. part 2. theory development and applications on modeling physicochemical properties of polyaromatic hydrocarbons
R Todeschini, P Gramatica, R Provenzani, and E Marengo · 1995
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Extending and benchmarking Cascade-Correlation: extensions to the Cascade-Correlation architecture and benchmarking of feed-forward supervised artificial neural networks
Samuel George Waugh · 1995
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Bagging predictors
Leo Breiman · 1996
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Experiments with a new boosting algorithm
Yoav Freund, Robert E Schapire, et al · 1996
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Shape quantization and recognition with randomized trees
Yali Amit and Donald Geman · 1997
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Modeling of strength of high-performance concrete using artificial neural networks
I-C Yeh · 1998
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Randomizing outputs to increase prediction accuracy
Leo Breiman · 2000
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An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
Thomas G Dietterich · 2000
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Instance-based classification by emerging patterns
Jinyan Li, Guozhu Dong, and Kotagiri Ramamohanarao · 2000
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Random Forests
Leo Breiman · 2001
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Atomic decomposition by basis pursuit
Scott Shaobing Chen, David L Donoho, and Michael A Saunders · 2001
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Classification and regression by randomforest
Andy Liaw, Matthew Wiener, et al · 2002
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Molecular descriptors influencing melting point and their role in classification of solid drugs
Christel AS Bergström, Ulf Norinder, Kristina Luthman, and Per Artursson · 2003
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Development of linear, ensemble, and nonlinear models for the prediction and interpretation of the biological activity of a set of pdgfr inhibitors
Rajarshi Guha and Peter C Jurs · 2004
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Assessing the reliability of a qsar model’s predictions
Linnan He and Peter C Jurs · 2005
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Survival ensembles
Torsten Hothorn, Peter Bühlmann, Sandrine Dudoit, Annette Molinaro, and Mark J Van Der Laan · 2005
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Gene selection and classification of microarray data using random forest
Ramón Díaz-Uriarte and Sara Alvarez De Andres · 2006
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Random forests and adaptive nearest neighbors
Yi Lin and Yongho Jeon · 2006
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Quantile regression forests
Nicolai Meinshausen · 2006
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Newer classification and regression tree techniques: bagging and random forests for ecological prediction
Anantha M Prasad, Louis R Iverson, and Andy Liaw · 2006
Cited alongside, same era.
Using random forests for handwritten digit recognition
Simon Bernard, Sébastien Adam, and Laurent Heutte · 2007
Cited alongside, same era.
Random forests for classification in ecology
D Richard Cutler, Thomas C Edwards Jr, Karen H Beard, Adele Cutler, Kyle T Hess, Jacob Gibson, and Joshua J Lawler · 2007
Cited alongside, same era.
Relaxed lasso
Nicolai Meinshausen · 2007
Cited alongside, same era.
Bias in random forest variable importance measures: Illustrations, sources and a solution
Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn · 2007
Cited alongside, same era.
Using crowd-source based features from social media and conventional features to predict the movies popularity
Mehreen Ahmed, Maham Jahangir, Hammad Afzal, Awais Majeed, and Imran Siddiqi · 2015
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Consistency of random forests
Erwan Scornet, Gérard Biau, Jean-Philippe Vert, et al · 2015
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Degrees of freedom and model search
Ryan J Tibshirani · 2015
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Reinforcement learning trees
Ruoqing Zhu, Donglin Zeng, and Michael R Kosorok · 2015
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A random forest guided tour
Gérard Biau and Erwan Scornet · 2016
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Neural random forests
Gérard Biau, Erwan Scornet, and Johannes Welbl · 2016
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Consistency of Random Forests and Other Averaging Classifiers
Gérard Biau, Luc Devroye, and Gábor Lugosi · 2008
Cited alongside, same era.
Random forests: some methodological insights
Robin Genuer, Jean-Michel Poggi, and Christine Tuleau · 2008
Cited alongside, same era.
Random survival forests
Hemant Ishwaran, Udaya B Kogalur, Eugene H Blackstone, Michael S Lauer, et al · 2008
Cited alongside, same era.
Conditional variable importance for random forests
Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis · 2008
Cited alongside, same era.
Influence of hyperparameters on random forest accuracy
Simon Bernard, Laurent Heutte, and Sébastien Adam · 2009
Cited alongside, same era.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
Cited alongside, same era.
Roxane Duroux and Erwan Scornet · 2016
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Integrative modelling of tumour dna methylation quantifies the contribution of metabolism
Mahya Mehrmohamadi, Lucas K Mentch, Andrew G Clark, and Jason W Locasale · 2016
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Quantifying uncertainty in random forests via confidence intervals and hypothesis tests
Lucas Mentch and Giles Hooker · 2016
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Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach
Sérgio Moro, Paulo Rita, and Bernardo Vala · 2016
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Random forests and kernel methods
Erwan Scornet · 2016
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Statistical inference on tree swallow migrations with random forests
Tim Coleman, Lucas Mentch, Daniel Fink, Frank La Sorte, Giles Hooker, Wesley Hochachka, and David Winkler · 2017
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Some asymptotic results of survival tree and forest models
Yifan Cui, Ruoqing Zhu, Mai Zhou, and Michael Kosorok · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Extended comparisons of best subset selection, forward stepwise selection, and the lasso
Trevor Hastie, Robert Tibshirani, and Ryan J Tibshirani · 2017
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Formal hypothesis tests for additive structure in random forests
Lucas Mentch and Giles Hooker · 2017
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To tune or not to tune the number of trees in random forest
Philipp Probst and Anne-Laure Boulesteix · 2017
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Tuning parameters in random forests
Erwan Scornet · 2017
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Explaining the success of adaboost and random forests as interpolating classifiers
Abraham J Wyner, Matthew Olson, Justin Bleich, and David Mease · 2017
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Making sense of random forest probabilities: a kernel perspective
Matthew A Olson and Abraham J Wyner · 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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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
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Scalable and efficient hypothesis testing with random forests
Tim Coleman, Wei Peng, and Lucas Mentch · 2019
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Please stop permuting features: An explanation and alternatives
Giles Hooker and Lucas Mentch · 2019
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The implicit regularization of ordinary least squares ensembles
Daniel LeJeune, Hamid Javadi, and Richard G Baraniuk · 2019
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Wei Peng, Tim Coleman, and Lucas Mentch · 2019
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ipred: Improved predictors, 2019
Andrea Peters, Torsten Hothorn, BD Ripley, T Therneau, and B Atkinson · 2019
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Hyperparameters and tuning strategies for random forest
Philipp Probst, Marvin N Wright, and Anne-Laure Boulesteix · 2019
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Censoring unbiased regression trees and ensembles
Jon Arni Steingrimsson, Liqun Diao, and Robert L Strawderman · 2019
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