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Prediction Rule Reshaping
Matt Bonakdarpour · Sabyasachi Chatterjee · Rina Barber · John Lafferty

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #89

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.

Author Information

Matt Bonakdarpour (University of Chicago)
Sabyasachi Chatterjee (Sabyasachi Chatterjee)
Rina Barber
John Lafferty (Yale University)

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