Calibration Of Neural Networks

# Calibration of Neural Networks

**arXiv ID:** 2303.10761
**Authors:** Ruslan Vasilev, Alexander D'yakonov
**Published:** 2023-03-19T20:27:51Z
**Abstract:**
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.

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## References

- [arXiv:2303.10761](http://arxiv.org/abs/2303.10761v1)