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This study investigates the impact of data quality on the performance of 19 machine learning algorithms across various tasks, emphasizing the importance of high-quality training and test data. It empirically examines six data quality dimensions and their effects on model reliability, providing insights into scenarios where data quality varies between training and testing phases. The findings highlight the necessity for a data-centric approach in AI development to ensure trustworthy outcomes.

Author

Fernanda Forbici

Language

EN