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arXiv · 2412.05882

Towards Modeling Data Quality and Machine Learning Model Performance

Usman Anjum·Chris Trentman·Elrod Caden·Justin Zhan
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Abstract

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.

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Usman Anjum, Chris Trentman, Elrod Caden, Justin Zhan. 2024-12-08. Towards Modeling Data Quality and Machine Learning Model Performance. https://arxiv.org/abs/2412.05882

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