Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution

Author name not available (Why is that?)

Publication date: 25 June 2018

Abstract: This paper examines a novel gradient boosting framework for regression. We regularize gradient boosted trees by introducing subsampling and employ a modified shrinkage algorithm so that at every boosting stage the estimate is given by an average of trees. The resulting algorithm, titled Boulevard, is shown to converge as the number of trees grows. We also demonstrate a central limit theorem for this limit, allowing a characterization of uncertainty for predictions. A simulation study and real world examples provide support for both the predictive accuracy of the model and its limiting behavior.

Has companion code repository: https://github.com/siriuz42/boulevard

This page was built for publication: Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6303492)