Advanced Methods for Training Gradient Boosting models in Multioutput Tasks

Student: Kovalev Oleg

Educational Programme: Data Science (Master)

Year of Graduation: 2024

Nowadays, there are two main approaches for using gradient boosting models in machine learning problems. The one-vs-all approach builds a number of trees equal to the output space dimension at each iteration. Another approach is called "single-tree" and at each iteration it builds only one multi-output tree is formed at each training stage. In this paper, we explored the possibility of improving the algorithms described above by using special processing of the model outputs at each iteration

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