Tabular Cleaning

We develop and evaluate an application-agnostic ML-based data cleaning approach using well-established imputation techniques for automated detection and cleaning of erroneous values. To improve the degree of automation, we combine imputation techniques with conformal prediction (CP), a model-agnostic and distribution-free method to quantify and calibrate the uncertainty of ML models.