Dealing with class imbalance

Machine learning models often face challenges when dealing with class imbalance, a common scenario where the number of examples in different classes is unequal, posing a risk of bias towards the majority class. This imbalance can lead to high accuracy but poor performance on minority classes, as illustrated by a hypothetical disease detection model with 99.9% accuracy that fails to identify rare cases. To address this, several strategies are available, including undersampling the majority class to balance datasets, oversampling or generating synthetic data to enrich minority classes, and employing cost-sensitive loss functions that penalize errors more heavily for minority classes. These approaches help models focus on learning from underrepresented data, ensuring better generalization and robustness. While the initial focus is on training models with unbalanced datasets, future discussions will explore evaluating models under such conditions.

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