# Prediction Rule Reshaping

arXiv: https://arxiv.org/abs/1805.06439 · HTML: https://mlsanity.com/paper/1805.06439/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2018.

## Built on by (most cited first, 3 of 3)

- [Explanations for Monotonic Classifiers](https://mlsanity.com/paper/2106.00154/) (2021)
- [Global Optimization Networks](https://mlsanity.com/paper/2202.01277/) (2022)
- [Monotone Tree-Based GAMI Models by Adapting XGBoost](https://mlsanity.com/paper/2309.02426/) (2023)

## Abstract (arXiv)

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.