.png){kind=icon}
Return
On sparse optimal regression trees
.png){kind=icon}
OA
AI
DOI：10.1016/j.ejor.2021.12.022.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between prediction accuracy and both types of sparsity, namely local and global, is sought. Our approach can accommodate important desirable properties for the regression task, such as cost-sensitivity and fairness. Thanks to the smoothness of the predictions, we can derive local explanations on the continuous predictor variables. The computational experience reported shows the outperformance of our approach in terms of prediction accuracy against standard benchmark regression methods such as CART, OLS and LASSO. Moreover, the scalability of our approach with respect to the size of the training sample is illustrated. (c) 2021 Published by Elsevier B.V.
Keywords:
Machine learning
Classification and regression trees
Optimal regression trees
Sparsity
Nonlinear programming
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
Organization
No organization information available
Cited Papers
Awareness of activity limitations, goal setting and rehabilitation outcome in patients with brain injuries.png)
Brain Injury
Brain InjuryIF0
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more