Prediction Intervals for Random Forests

Implements multiple state-of-the-art prediction interval methodologies for random forests. These include: quantile regression intervals, out-of-bag intervals, bag-of-observations intervals, one-step boosted random forest intervals, bias-corrected intervals, high-density intervals, and split-conformal intervals. The implementations include a combination of novel adjustments to the original random forest methodology and novel prediction interval methodologies. All of these methodologies can be utilized using solely this package, rather than a collection of separate packages. Currently, only regression trees are supported. Also capable of handling high dimensional data. Roy, Marie-Helene and Larocque, Denis (2019) . Ghosal, Indrayudh and Hooker, Giles (2018) . Zhu, Lin and Lu, Jiaxin and Chen, Yihong (2019) . Zhang, Haozhe and Zimmerman, Joshua and Nettleton, Dan and Nordman, Daniel J. (2019) . Meinshausen, Nicolai (2006) < http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>. Romano, Yaniv and Patterson, Evan and Candes, Emmanuel (2019) . Tung, Nguyen Thanh and Huang, Joshua Zhexue and Nguyen, Thuy Thi and Khan, Imran (2014) .


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install.packages("piRF")

0.1.0 by Chancellor Johnstone, 5 months ago


http://github.com/chancejohnstone/piRF


Browse source code at https://github.com/cran/piRF


Authors: Chancellor Johnstone [cre, aut, cph] , Haozhe Zhang [aut, cph] , Martin Wright [ctb, cph] , Gregor DeCillia [ctb, cph]


Documentation:   PDF Manual  


GPL-3 license


Imports Rdpack

Suggests testthat, devtools, foreach, doParallel, hdrcde, rfinterval, ranger


See at CRAN