# Towards Modeling Data Quality and Machine Learning Model Performance

arXiv: https://arxiv.org/abs/2412.05882 · HTML: https://mlsanity.com/paper/2412.05882/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 8 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks](https://mlsanity.com/paper/2009.05835/) (2020)
- [Principles for Evaluation of AI/ML Model Performance and Robustness](https://mlsanity.com/paper/2107.02868/) (2021)
- [TBAM: Towards An Agent-Based Model to Enrich Twitter Data](https://mlsanity.com/paper/2302.00128/) (2023)

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

- [From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems](https://mlsanity.com/paper/2605.23955/) (2026)

## Abstract (arXiv)

Understanding the effect of uncertainty and noise in data on machine learning models (MLM) is crucial in developing trust and measuring performance. In this paper, a new model is proposed to quantify uncertainties and noise in data on MLMs. Using the concept of signal-to-noise ratio (SNR), a new metric called deterministic-non-deterministic ratio (DDR) is proposed to formulate performance of a model. Using synthetic data in experiments, we show how accuracy can change with DDR and how we can use DDR-accuracy curves to determine performance of a model.