Prediction Rule Reshaping
This work addresses the need for interpretable and reliable models in fields like economics or medicine by ensuring shape constraints, though it is incremental as it builds on existing pre-trained rules.
The authors tackled the problem of enforcing shape constraints like monotonicity and convexity in high-dimensional regression and classification by proposing two methods that reshape pre-trained prediction rules, including one for random forests, and found that these methods enforce constraints without compromising predictive accuracy.
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.