Social Acceptance of AI

SMOTE (Synthetic Minority Over-sampling Technique) is a method used to address class imbalance in machine learning. It creates new, synthetic examples of the minority class by interpolating between existing minority samples. This helps improve model performance and generalization by balancing the dataset, reducing bias toward the majority class, and enhancing the classifier’s ability to detect rare but important instances.