Forest Guided Smoothing
Machine Learning
2021-03-10 v1 Machine Learning
Methodology
Abstract
We use the output of a random forest to define a family of local smoothers with spatially adaptive bandwidth matrices. The smoother inherits the flexibility of the original forest but, since it is a simple, linear smoother, it is very interpretable and it can be used for tasks that would be intractable for the original forest. This includes bias correction, confidence intervals, assessing variable importance and methods for exploring the structure of the forest. We illustrate the method on some synthetic examples and on data related to Covid-19.
Keywords
Cite
@article{arxiv.2103.05092,
title = {Forest Guided Smoothing},
author = {Isabella Verdinelli and Larry Wasserman},
journal= {arXiv preprint arXiv:2103.05092},
year = {2021}
}