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文件名称:Tree Boosting With XGBoost
文件大小:2.12MB
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更新时间:2020-09-29 03:14:26
Tree Boosting With XGBoost
Tree boosting has empirically proven to be a highly effective approach to predictive
modeling. It has shown remarkable results for a vast array of problems. For many
years, MART has been the tree boosting method of choice. More recently, a tree
boosting method known as XGBoost has gained popularity by winning numerous
machine learning competitions.
In this thesis, we will investigate how XGBoost differs from the more traditional
MART. We will show that XGBoost employs a boosting algorithm which we will
term Newton boosting. This boosting algorithm will further be compared with the
gradient boosting algorithm that MART employs. Moreover, we will discuss the
regularization techniques that these methods offer and the effect these have on the
models.