This article argues that raw scores are insufficient for comparing machine learning models, as they can be misleading. It introduces the concept of calibration as a method to ensure fair comparisons of predictions across different ML systems. By understanding calibration, users can gain a more accurate assessment of model performance. AI
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IMPACT Highlights the importance of proper model evaluation techniques beyond raw scores for accurate system comparisons.
RANK_REASON The article discusses a technical concept (model calibration) relevant to machine learning research and practice. [lever_c_demoted from research: ic=1 ai=1.0]
