How do you handle an imbalanced dataset?

Question

Accepted Answer

Handling imbalanced datasets involves techniques like resampling, using different algorithms, and adjusting class weights.

Use resampling techniques like oversampling the minority class or undersampling the majority class.
Utilize algorithms that are robust to imbalanced datasets, such as Random Forest, XGBoost, or SVM.
Adjust class weights in the model to give more importance to the minority class.
Use techniques like SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for the minority class.
Consider ensemble methods like EasyEnsemble or BalanceCascade for handling imbalanced datasets.