How do you address skewed data distributions in machine learning models?

To address skewed distributions, you can use techniques like resampling (oversampling minority class or undersampling majority class), log transformations, or using algorithms that handle class imbalance (e.g., decision trees, random forests). For example, Resampling in an imbalanced dataset to ensure fair model learning.