Boosted decision trees

TL;DR

This paper discusses boosted decision trees, highlighting their effectiveness, how they are trained and evaluated, and how ensemble methods like AdaBoost and gradient boosting improve their performance, with applications in high-energy physics.

Contribution

It introduces boosting algorithms for decision trees and demonstrates their advantages and applications in high-energy physics contexts.

Findings

Boosted decision trees significantly improve classification performance.

Ensemble methods like AdaBoost and gradient boosting mitigate decision trees' shortcomings.

Applications in high-energy physics showcase practical benefits.

Abstract

Boosted decision trees are a very powerful machine learning technique. After introducing specific concepts of machine learning in the high-energy physics context and describing ways to quantify the performance and training quality of classifiers, decision trees are described. Some of their shortcomings are then mitigated with ensemble learning, using boosting algorithms, in particular AdaBoost and gradient boosting. Examples from high-energy physics and software used are also presented.