Prediction Rule Reshaping
Machine Learning
2018-05-17 v1 Machine Learning
Abstract
Two methods are proposed for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The first method can be applied to any pre-trained prediction rule, while the second method deals specifically with random forests. In both cases, efficient algorithms are developed for computing the estimators, and experiments are performed to demonstrate their performance on four datasets. We find that reshaping methods enforce shape constraints without compromising predictive accuracy.
Cite
@article{arxiv.1805.06439,
title = {Prediction Rule Reshaping},
author = {Matt Bonakdarpour and Sabyasachi Chatterjee and Rina Foygel Barber and John Lafferty},
journal= {arXiv preprint arXiv:1805.06439},
year = {2018}
}