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We use the output of a random forest to define a family of local smoothers with spatially adaptive bandwidth matrices.
Multivariate locally weighted least squares regression
David Ruppert and Matthew P Wand · 1994
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An introduction to multivariate adaptive regression splines, 1995
Jerome H Friedman and Charles B Roosen · 1995
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Empirical-bias bandwidths for local polynomial nonparametric regression and density estimation
David Ruppert · 1997
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Randomizing outputs to increase prediction accuracy
Leo Breiman · 2000
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Scale space view of curve estimation
Probal Chaudhuri and James Steven Marron · 2000
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Bagging survival trees
Torsten Hothorn, Berthold Lausen, Axel Benner, and Martin Radespiel-Tröger · 2004
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Extremely randomized trees
Pierre Geurts, Damien Ernst, and Louis Wehenkel · 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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Generalized jackknife estimators of weighted average derivatives
Matias D Cattaneo, Richard K Crump, and Michael Jansson · 2013
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Confidence intervals for random forests: The jackknife and the infinitesimal jackknife
Stefan Wager, Trevor Hastie, and Bradley Efron · 2014
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Supervised neighborhoods for distributed nonparametric regression
Adam Bloniarz, Ameet Talwalkar, Bin Yu, and Christopher Wu · 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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Random forests and kernel methods
Erwan Scornet · 2016
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Model agnostic supervised local explanations
Gregory Plumb, Denali Molitor, and Ameet S Talwalkar · 2018
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Generalized random forests
Susan Athey, Julie Tibshirani, Stefan Wager, et al · 2019
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Asymptotic distributions and rates of convergence for random forests via generalized u-statistics
Wei Peng, Tim Coleman, and Lucas Mentch · 2019
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Local linear forests
Rina Friedberg, Julie Tibshirani, Susan Athey, and Stefan Wager · 2020
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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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A unified approach for inference on algorithm-agnostic variable importance
Brian D Williamson, Peter B Gilbert, Noah R Simon, and Marco Carone · 2020
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