Challenging Machine Learning Dogmas through Reproduction Studies

This chapter explores a reproduction study that challenges the conventional belief that reliance on static benchmarks in machine learning leads to overfitting. The findings suggest that higher scores on evaluation sets correlate with greater effectiveness on new data, prompting a deeper discussion on the philosophical underpinnings and quality of statistical practices.

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