TY - GEN
T1 - Prediction rule reshaping
AU - Bonakdarpour, Matt
AU - Chatterjee, Sabyasachi
AU - Barber, Rina Foygel
AU - Lafferty, John
N1 - Publisher Copyright:
© 2018 by the Authors. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85057246403&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057246403&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85057246403
T3 - 35th International Conference on Machine Learning, ICML 2018
SP - 1014
EP - 1022
BT - 35th International Conference on Machine Learning, ICML 2018
A2 - Dy, Jennifer
A2 - Krause, Andreas
PB - International Machine Learning Society (IMLS)
T2 - 35th International Conference on Machine Learning, ICML 2018
Y2 - 10 July 2018 through 15 July 2018
ER -