Performance under data corruption follows an exponential diminishing-return curve, noise harms more than missingness, imputation helps only when accurate, and adding data cannot fully compensate.
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Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies
Performance under data corruption follows an exponential diminishing-return curve, noise harms more than missingness, imputation helps only when accurate, and adding data cannot fully compensate.