How to handle imbalanced datasets in classification

Short answer

Use oversampling (SMOTE), undersampling, or class weights to balance classes. Choose metrics like precision, recall, and F1 instead of accuracy. For evaluating your approach, see how to evaluate machine learning model performance. For preparing data before balancing, see how to preprocess data for machine learning models.

Steps

1. Analyze class distribution
2. Choose a strategy: oversample minority, undersample majority, or class weights
3. Apply SMOTE or similar techniques on training data only
4. Train the classifier with adjusted weights if needed
5. Evaluate with precision, recall, and F1-score

Tips

• Never apply resampling before splitting train and test sets
• Use stratified cross-validation to maintain class ratios
• Understand the bias-variance tradeoff in machine learning when adjusting sampling