SMOTE (Synthetic Minority Over-sampling Technique)

SMOTE (Synthetic Minority Over-sampling Technique) is a method used to address imbalanced datasets in machine learning, where one category (the minority class) has fewer examples than others. Instead of just copying existing minority samples, SMOTE creates new, artificial examples by interpolating between existing ones. It does this by selecting a data point from the minority class and generating new points along the line connecting it to its nearest neighbors within the same class. This approach helps the model learn better by providing more balanced and representative data, improving its ability to identify the minority class accurately.