Boosted decision trees
Data Analysis, Statistics and Probability
2022-06-22 v1 High Energy Physics - Experiment
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.
Cite
@article{arxiv.2206.09645,
title = {Boosted decision trees},
author = {Yann Coadou},
journal= {arXiv preprint arXiv:2206.09645},
year = {2022}
}
Comments
46 pages, 12 figures. To appear in Artificial Intelligence for High Energy Physics, World Scientific Publishing, 2022