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Explaining the Paradox of Highly Flexible Models in Tabular Machine Learning

Modern machine learning models sometimes achieve state-of-the-art performance on high-dimensional, collinear, error-prone data despite the dominant paradigm suggesting inaccurate predictions. This paradox lacks a comprehensively satisfying explanation within ML's own theoretical framework.

Computing & Technology, Computer Science, Machine Learning