The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling

TL;DR

This paper reviews the evolution of boosting algorithms from their origins in machine learning to their application in statistical modelling, emphasizing methodological similarities and interpretability benefits.

Contribution

It provides a comprehensive overview of boosting algorithms, comparing AdaBoost, gradient boosting, and likelihood-based boosting, and discusses their software implementations and interpretability.

Findings

Statistical boosting offers more interpretable models than initial machine learning algorithms.

Gradient boosting and likelihood-based boosting share fundamental concepts despite different applications.

The paper includes practical R code examples for implementing boosting methods.

Abstract

The concept of boosting emerged from the field of machine learning. The basic idea is to boost the accuracy of a weak classifying tool by combining various instances into a more accurate prediction. This general concept was later adapted to the field of statistical modelling. This review article attempts to highlight this evolution of boosting algorithms from machine learning to statistical modelling. We describe the AdaBoost algorithm for classification as well as the two most prominent statistical boosting approaches, gradient boosting and likelihood-based boosting. Although both appraoches are typically treated separately in the literature, they share the same methodological roots and follow the same fundamental concepts. Compared to the initial machine learning algorithms, which must be seen as black-box prediction schemes, statistical boosting result in statistical models which offer a straight-forward interpretation. We highlight the methodological background and present the most common software implementations. Worked out examples and corresponding R code can be found in the Appendix.