​ 【论文】硕士论文：XGBoost提升树——为什么XGBoost能“横扫”机器学习竞赛

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点击上方 “机器学习研究会” 可以订阅哦 摘要   转自：爱可可-爱生活 硕士论文《Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition?》摘要： 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 emplo ………………………………