What is class imbalance and how do you handle it?

Class imbalance occurs when one class dominates the dataset, affecting model performance.

In imbalanced datasets, models tend to bias toward majority classes. This leads to misleading accuracy. Techniques include resampling (oversampling minority, undersampling majority), using class weights, and anomaly detection approaches. Metrics like F1-score and ROC-AUC are preferred over accuracy.