SMOTE

SMOTE, or Synthetic Minority Over-sampling Technique, is a statistical method used to address class imbalance in datasets. It works by generating synthetic samples from the minority class, effectively increasing its presence in the dataset. This is achieved by interpolating between existing minority class samples, which helps to create a more balanced distribution of classes. SMOTE is commonly used in various machine learning applications, particularly in classification tasks where the minority class is underrepresented. By improving the representation of minority classes, SMOTE can enhance model performance and accuracy.