What are the best algorithms for handling imbalanced data?

What are the best algorithms for handling imbalanced data?

Some of the best algorithms for imbalanced data include Random Forest, SMOTE, and XGBoost. Would you like to know more about any specific one?

Can you explain how SMOTE works?

SMOTE, or Synthetic Minority Over-sampling Technique, generates synthetic samples for the minority class by interpolating between existing samples. This helps balance the dataset.

Are there any specific metrics I should use to evaluate models on imbalanced data?

Yes, metrics like F1-score, precision-recall curve, and AUC-ROC are more informative than accuracy for imbalanced datasets.

What preprocessing steps should I take before applying these algorithms?

Preprocessing steps include handling missing values, normalizing or standardizing features, and applying techniques like SMOTE for balancing.