<ul><li>Data corruption, including missing and noisy data, presents challenges in machine learning.</li><li>Study explores strategies to mitigate effects of data corruption through supervised learning with NLP tasks and deep reinforcement learning for traffic signal optimization.</li><li>Analysis shows model performance under data corruption follows a diminishing return curve and noisy data causes severe performance degradation, especially in sequential decision-making tasks.</li><li>Increasing dataset size helps mitigate effects of data corruption, but a rule emerges that approximately 30% of data is critical for determining performance.</li></ul>

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

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