The effects of data quality on machine learning performance on tabular data

By Sedir Mohammed, Lukas Budach, Moritz Feuerpfeil, Nina Ihde, Andrea Nathansen, Nele Sina Noack, Hendrik Patzlaff, Felix Naumann, Hazar Harmouch. 2022

161 citations recorded by the source corpus.

Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example, incomplete, erroneous, or inappropriate training data can lead to unreliable models that produce ultimately poor decisions. Trustworthy AI applications require high-quality training and test data along many quality dimensions, such as accuracy, completeness, and consistency. We explore empirically the relationship between six data quality dimensions and the performance of 19 popular machine learning algorithms covering the tasks of classification, regression, and clustering, with the goal of explaining their performance in terms of data quality. Our experiments distinguish three scenarios based on the AI pipeline steps that were fed with polluted data: polluted training data, test data, or both. We conclude the paper with an extensive discussion of our observations.

Subfields: Artificial Intelligence, Management Science and Operations Research, Evaluation & Benchmarks

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In Artificial Intelligence

1. Keith Stevens
2. Christoph Schuhmann
3. ES Shahul
4. Yannic Kilcher

Citations in the corpus

• OpenAssistant Conversations - Democratizing Large Language Model Alignment (2023, positive): “It becomes apparent that the benefits of all the previous aforementioned stages, are predominantly imposed by the quality of the data used [7].”