增强决策树

Y. Coadou
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引用次数: 26

摘要

增强决策树是一种非常强大的机器学习技术。在介绍了高能物理背景下机器学习的具体概念并描述了量化分类器的性能和训练质量的方法之后,描述了决策树。它们的一些缺点可以通过集成学习来缓解,使用增强算法,特别是AdaBoost和梯度增强。本文还介绍了高能物理的实例和所使用的软件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Boosted Decision Trees
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.
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