Prediction Rule Reshaping
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
Text
Bonakdarpour et al. "Prediction Rule Reshaping." International Conference on Machine Learning, 2018.Markdown
[Bonakdarpour et al. "Prediction Rule Reshaping." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/bonakdarpour2018icml-prediction/)BibTeX
@inproceedings{bonakdarpour2018icml-prediction,
title = {{Prediction Rule Reshaping}},
author = {Bonakdarpour, Matt and Chatterjee, Sabyasachi and Barber, Rina Foygel and Lafferty, John},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {630-638},
volume = {80},
url = {https://mlanthology.org/icml/2018/bonakdarpour2018icml-prediction/}
}