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Activity Number: 561 - Small Data, Big Impact
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract #304866 Presentation
Title: Gradient Boosting Trees for Spatial Data Prediction
Author(s): Bo Li* and Peng Wang and Yunzhang Zhu
Companies: University of Illinois at Urbana-Champaign and University of Cincinnati and The Ohio State University
Keywords: Gradient Boosting Tree; Machine Learning; Spatial data

In recent years, statistical machine learning approaches have been extremely popular largely due to its superior performance in prediction. Of all the commonly used machine learning tools, the gradient boosting tree is usually the favored vehicle for many practitioners. On the popular data analytics competition platform Kaggle, gradient boosting is the winning algorithm for almost every structured data. Besides its superior prediction performance, the gradient boosting trees also enjoy the interpretablility of a non-parametric additive model and its fitting algorithm can be paralleled. In this project, we extend this powerful machine learning technique to the realm of spatial data analysis. The proposed approach does not require any parametric assumption on spatial correlations and enjoy all the advantages of gradient boosting. We illustrate the potential of the data with application on prediction of HIV new diagnose rates for all counties of the United States

Authors who are presenting talks have a * after their name.

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