Method for Improving Gradient Boosting Learning Efficiency Based on Modified Loss Functions

被引：0
|
作者：
Korolev, N. S. [1 ]
Senko, O. V. [2 ]
机构：
[1] Lomonosov Moscow State Univ, Moscow 119991, Russia
[2] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Moscow 119333, Russia
来源：
AUTOMATION AND REMOTE CONTROL | 2022年 / 83卷 / 12期
关键词：
gradient boosting; decision tree; loss function; machine learning; data analysis;
D O I：
10.1134/S00051179220120074
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We consider a new method to improve the quality of training in gradient boosting as wellas to increase its generalization performance based on the use of modified loss functions. Incomputational experiments, the possible applicability of this method to improve the quality ofgradient boosting when solving various classification and regression problems on real data isshown.
引用
收藏
页码：1935 / 1943
页数：9
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