# New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences

arXiv: https://arxiv.org/abs/2209.15588 · HTML: https://mlsanity.com/paper/2209.15588/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Error-Bounded Correction of Noisy Labels](https://mlsanity.com/paper/2011.10077/) (2020)
- [Deep k-NN for Noisy Labels](https://mlsanity.com/paper/2004.12289/) (2020)
- [Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks](https://mlsanity.com/paper/2008.13547/) (2020)
- [Jet-Images -- Deep Learning Edition](https://mlsanity.com/paper/1511.05190/) (2015)
- [Estimating Cosmological Parameters from the Dark Matter Distribution](https://mlsanity.com/paper/1711.02033/) (2017)
- [A Cautionary Tale of Decorrelating Theory Uncertainties](https://mlsanity.com/paper/2109.08159/) (2021)
- [Deep Learning the Ising Model Near Criticality](https://mlsanity.com/paper/1708.04622/) (2017)
- [Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing](https://mlsanity.com/paper/2007.13875/) (2020)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Evaluation Function: A Multi-Metric Approach for Optimizing Demand Forecasting Models](https://mlsanity.com/paper/2508.13057/) (2025)

## Abstract (arXiv)

The application of machine learning to physics problems is widely found in the scientific literature. Both regression and classification problems are addressed by a large array of techniques that involve learning algorithms. Unfortunately, the measurement errors of the data used to train machine learning models are almost always neglected. This leads to estimations of the performance of the models (and thus their generalisation power) that is too optimistic since it is always assumed that the target variables (what one wants to predict) are correct. In physics, this is a dramatic deficiency as it can lead to the belief that theories or patterns exist where, in reality, they do not. This paper addresses this deficiency by deriving formulas for commonly used metrics (both for regression and classification problems) that take into account measurement errors of target variables. The new formulas give an estimation of the metrics which is always more pessimistic than what is obtained with the classical ones, not taking into account measurement errors. The formulas given here are of general validity, completely model-independent, and can be applied without limitations. Thus, with statistical confidence, one can analyze the existence of relationships when dealing with measurements with errors of any kind. The formulas have wide applicability outside physics and can be used in all problems where measurement errors are relevant to the conclusions of studies.