Boosting(机器学习)
计算机科学
机器学习
人工智能
交替决策树
决策树
梯度升压
推论
树(集合论)
支持向量机
算法
数学
增量决策树
决策树学习
随机森林
数学分析
作者
Natalia Ponomareva,Thomas Colthurst,Gilbert Hendry,Salem Haykal,Soroush Radpour
标识
DOI:10.1109/bigdata.2017.8257910
摘要
Gradient boosted decision trees are a popular machine learning technique, in part because of their ability to give good accuracy with small models. We describe two extensions to the standard tree boosting algorithm designed to increase this advantage. The first improvement extends the boosting formalism from scalar-valued trees to vector-valued trees. This allows individual trees to be used as multiclass classifiers, rather than requiring one tree per class, and drastically reduces the model size required for multiclass problems. We also show that some other popular vector-valued gradient boosted trees modifications fit into this formulation and can be easily obtained in our implementation. The second extension, layer-by-layer boosting, takes smaller steps in function space, which is empirically shown to lead to a faster convergence and to a more compact ensemble. We have added both improvements to the open-source TensorFlow Boosted trees (TFBT) package, and we demonstrate their efficacy on a variety of multiclass datasets. We expect these extensions will be of particular interest to boosted tree applications that require small models, such as embedded devices, applications requiring fast inference, or applications desiring more interpretable models.
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