Handling Imbalanced Data

#Learning Objectives

• Implement SMOTE from scratch and explain how synthetic oversampling differs from random duplication
• Evaluate imbalanced classifiers using F1, AUPRC, and Matthews Correlation Coefficient instead of accuracy
• Compare class weighting, threshold tuning, and resampling strategies and select the right approach for a given imbalance ratio
• Build a complete imbalanced data pipeline that combines SMOTE, class weights, and threshold optimization