Autor:	Cief, Matej, Kveton, Branislav, Kompan, Michal
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this paper, we study the problem of estimator selection and hyper-parameter tuning in off-policy evaluation. Although cross-validation is the most popular method for model selection in supervised learning, off-policy evaluation relies mostly on theory-based approaches, which provide only limited guidance to practitioners. We show how to use cross-validation for off-policy evaluation. This challenges a popular belief that cross-validation in off-policy evaluation is not feasible. We evaluate our method empirically and show that it addresses a variety of use cases. Comment: 13 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.15332 Zobrazit plný text záznamu View this record from Arxiv