How to handle imbalanced datasets?
Updated May 17, 2026
Short answer
Use resampling, class weights, or specialized metrics.
Deep explanation
Techniques include oversampling, undersampling, SMOTE, and weighted loss functions.
Real-world example
Fraud detection datasets with rare fraud cases.
Common mistakes
- Using accuracy as main metric.
Follow-up questions
- What is SMOTE?
- Why class weights?