The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles
arXiv:2606.29720v1 Announce Type: new 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 degrades probability calibration by distorting the training prior [1]. We extend this lens to synthetic oversampling (SMOTE) and provide a practical, evidence-based guide to when calibration damage matters and how to fix it. Across five public datasets (imbalance ratio 1.9-70) and two ensemble models (random forest, gradient b
This research is published as AI models become more sophisticated and are deployed in real-world, high-stakes scenarios where nuanced performance metrics beyond simple accuracy are crucial.
It highlights a critical but often overlooked aspect of AI model development for imbalanced datasets, directly impacting the reliability and trustworthiness of AI systems, especially in applications requiring accurate probability predictions.
The understanding of how common imbalance correction methods can degrade probability calibration, necessitating more sophisticated evaluation and mitigation strategies in AI development and deployment.
- · AI researchers specializing in robust model calibration
- · Developers of production-grade AI systems
- · Sectors reliant on precise probability predictions (e.g., finance, healthcare)
- · AI development teams relying solely on basic resampling for imbalanced data
- · Models deployed without calibration awareness
- · Benchmarks focused only on F1/accuracy for imbalanced data
Increased focus on post-hoc calibration techniques and more complex data sampling strategies in AI model training.
Development of new open-source tools and libraries specifically designed to assess and improve probability calibration in imbalanced datasets.
Regulatory bodies potentially incorporating requirements for calibration performance in AI model certifications for high-risk applications.
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Read at arXiv cs.LG