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Nowadays, machine learning (ML) plays a vital role in many aspects of ourdaily life. In essence, building well-performing ML applications requires theprovision of high-quality data throughout the entire life-cycle of suchapplications. Nevertheless, most of the real-world tabular data suffer fromdifferent types of discrepancies, such as missing values, outliers, duplicates,pattern violation, and inconsistencies. Such discrepancies typically emergewhile collecting, transferring, storing, and/or integrating the data. To dealwith these discrepancies, numerous data cleaning methods have been introduced.However, the majority of such methods broadly overlook the requirements imposedby downstream ML models. As a result, the potential of utilizing these datacleaning methods in ML pipelines is predominantly unrevealed. In this work, weintroduce a comprehensive benchmark, called REIN1, to thoroughly investigatethe impact of data cleaning methods on various ML models. Through thebenchmark, we provide answers to important research questions, e.g., where andwhether data cleaning is a necessary step in ML pipelines. To this end, thebenchmark examines 38 simple and advanced error detection and repair methods.To evaluate these methods, we utilized a wide collection of ML models trainedon 14 publicly-available datasets covering different domains and encompassingrealistic as well as synthetic error profiles.

REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines