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Detecting Errors in Numerical Data via any Regression Model
Hang Zhou · Jonas Mueller · Mayank Kumar · Jane-Ling Wang · Jing Lei

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in DMLR Workshop: Data-centric Machine Learning Research »

Noise plagues many numerical datasets, where the recorded values in the data may fail to match the true underlying values due to reasons including: erroneous sensors, data entry/processing mistakes, or imperfect human estimates. Here we consider estimating which data values are incorrect along a numerical column. We present a model-agnostic approach that can utilize any regressor (i.e. statistical or machine learning model) which was fit to predict values in this column based on the other variables in the dataset. By accounting for various uncertainties, our approach distinguishes between genuine anomalies and natural data fluctuations, conditioned on the available information in the dataset. We establish theoretical guarantees for our method and show that other approaches like conformal inference struggle to detect errors. We also contribute a new error detection benchmark involving 5 regression datasets with real-world numerical errors (for which the true values are also known). In this benchmark and additional simulation studies, our method identifies incorrect values with better precision/recall than other approaches.

Author Information

Hang Zhou (UC Davis)
Jonas Mueller (Cleanlab)
Mayank Kumar (Cleanlab)
Jane-Ling Wang (UC Davis)
Jing Lei (Carnegie Mellon University)

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