<ul><li>Data corruption, including missing and noisy data, poses challenges in machine learning.</li><li>This study investigates the effects of data corruption on model performance and explores strategies to mitigate them.</li><li>Results show that noisy data has a more severe impact than missing data, especially in sequential decision-making tasks.</li><li>Increasing dataset size helps mitigate but cannot fully overcome the effects of data corruption.</li></ul>

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies

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