genWeights

<p>This function is primarily meant to be used within the TungUbRF() function. All parameters are same as in TungUbRF().</p>

internal

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) <doi:10.1177/0962280219829885>.
Ghosal, Indrayudh and Hooker, Giles (2018) <arXiv:1803.08000>.
Zhu, Lin and Lu, Jiaxin and Chen, Yihong (2019) <arXiv:1905.10101>.
Zhang, Haozhe and Zimmerman, Joshua and Nettleton, Dan and Nordman, Daniel J. (2019) <doi:10.1080/00031305.2019.1585288>.
Meinshausen, Nicolai (2006) <http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>.
Romano, Yaniv and Patterson, Evan and Candes, Emmanuel (2019) <arXiv:1905.03222>.
Tung, Nguyen Thanh and Huang, Joshua Zhexue and Nguyen, Thuy Thi and Khan, Imran (2014) <doi:10.13140/2.1.2500.8002>.

Chancellor Johnstone

piRF

Prediction Intervals for Random Forests

Haozhe Zhang

Martin Wright

Gregor DeCillia

genWeights function

Generate weights for RF through feature bias reduction method outlined in Tung 2014. — genWeights

Generate weights for RF through feature bias reduction method outlined in Tung 2014.

genWeights: Generate weights for RF through feature bias reduction method outlined in Tung 2014.

Description

Usage

Arguments