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 \emph{which} data values are incorrect along a numerical column. We present a model-agnostic approach that can utilize \emph{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.

在数据中加入噪声，可以使用任何回归器模型来判断哪些数据值是错误的，该方法可以在数据集中区分真正的异常和自然数据波动，并且可以比传统方法更好地诊断真实的数据集。

通过任何回归模型检测数值数据中的错误