Convex Regression with Interpretable Sharp Partitions
Abstract
We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data- adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low- variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set.
Cite
Text
Petersen et al. "Convex Regression with Interpretable Sharp Partitions." Journal of Machine Learning Research, 2016.Markdown
[Petersen et al. "Convex Regression with Interpretable Sharp Partitions." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/petersen2016jmlr-convex/)BibTeX
@article{petersen2016jmlr-convex,
title = {{Convex Regression with Interpretable Sharp Partitions}},
author = {Petersen, Ashley and Simon, Noah and Witten, Daniela},
journal = {Journal of Machine Learning Research},
year = {2016},
pages = {1-31},
volume = {17},
url = {https://mlanthology.org/jmlr/2016/petersen2016jmlr-convex/}
}