论文标题
提升决策树
Boosted decision trees
论文作者
论文摘要
提升决策树是一种非常强大的机器学习技术。在高能物理环境中介绍了机器学习的特定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。然后，使用增强算法，尤其是Adaboost和梯度提升，通过集合学习来减轻他们的一些缺点。还提供了来自高能物理和软件的示例。
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.
