On Boosting: Theory and Applications

39 Pages Posted: 20 Jun 2019

See all articles by Andrea Ferrario

Andrea Ferrario

Dep. Management, Technology, and Economics ETH Zurich; Mobiliar Lab for Analytics at ETH

Roger Hämmerli

Schweizerische Mobiliar Versicherungsgesellschaft

Date Written: June 11, 2019

Abstract

We provide an overview of two commonly used boosting methodologies. We start with the description of different implementations as well as the statistical theory behind selected algorithms which are widely used by the machine learning community, then we discuss a case study focusing on the prediction of car insurance claims in a fixed future time interval. The results of the case study show that, overall, XGBoost performs better than AdaBoost and it shows best performance when shallow trees, moderate shrinking, the number of iterations increased with respect to default as well as subsampling of both features and training data points are considered.

Keywords: machine learning, boosting, predictive modeling, R, Python, car insurance, Kaggle, Porto Seguro, AdaBoost, XGBoost

Suggested Citation

Ferrario, Andrea and Hämmerli, Roger, On Boosting: Theory and Applications (June 11, 2019). Available at SSRN: https://ssrn.com/abstract=3402687 or http://dx.doi.org/10.2139/ssrn.3402687

Andrea Ferrario (Contact Author)

Dep. Management, Technology, and Economics ETH Zurich ( email )

Zurich
Switzerland

Mobiliar Lab for Analytics at ETH ( email )

Zürich, 8092
Switzerland

Roger Hämmerli

Schweizerische Mobiliar Versicherungsgesellschaft ( email )

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