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Resampling methods degrade model calibration, but recalibration offers a fix

By PulseAugur Editorial · [2 sources] ·

A new research paper published on arXiv explores the impact of resampling methods on the probability calibration of tree ensemble models. The study found that while SMOTE (Synthetic Minority Over-sampling Technique) causes a small degradation in calibration, random undersampling poses a significant risk, especially with high imbalance ratios, by distorting training data and making probability estimation unreliable. Fortunately, post-hoc recalibration techniques like Platt or isotonic scaling can effectively eliminate this calibration damage with minimal impact on discrimination performance. AI

IMPACT Highlights the importance of probability calibration in imbalanced datasets and offers practical solutions for practitioners.

RANK_REASON The cluster contains a research paper detailing findings on machine learning model calibration.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Resampling methods degrade model calibration, but recalibration offers a fix

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Zewen Liu ·

    The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles

    arXiv:2606.29720v1 Announce Type: cross Abstract: Resampling methods such as SMOTE and random under/over-sampling are standard tools for class-imbalanced classification, almost always evaluated by minority-class accuracy or F1. Prior work has established that undersampling degrad…

  2. arXiv cs.CL TIER_1 English(EN) · Zewen Liu ·

    The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles

    Resampling methods such as SMOTE and random under/over-sampling are standard tools for class-imbalanced classification, almost always evaluated by minority-class accuracy or F1. Prior work has established that undersampling degrades probability calibration by distorting the train…

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