|
ИСТИНА |
Войти в систему Регистрация |
ИСТИНА ИНХС РАН |
||
Calibration of Neural Networksстатья
- Авторы: Vasilev Ruslan, Дьяконов А.Г.
- Журнал: ArXiv e-prints
- Год издания: 2023
- DOI: 10.48550/ARXIV.2303.10761
- Аннотация: Neural networks solving real-world problems are often required not only to make accurate predictions but also to provide a confidence level in the forecast. The calibration of a model indicates how close the estimated confidence is to the true probability. This paper presents a survey of confidence calibration problems in the context of neural networks and provides an empirical comparison of calibration methods. We analyze problem statement, calibration definitions, and different approaches to evaluation: visualizations and scalar measures that estimate whether the model is well-calibrated. We review modern calibration techniques: based on post-processing or requiring changes in training. Empirical experiments cover various datasets and models, comparing calibration methods according to different criteria.
- Добавил в систему: Дьяконов Александр Геннадьевич