SMOTE

SMOTE (Synthetic Minority Over-sampling Technique) is a data augmentation technique used in imbalanced datasets to increase the number of instances in the minority class. It works by creating synthetic samples that are similar to the existing minority class instances. SMOTE selects a minority class instance and finds its k nearest neighbors. It then generates new instances by interpolating between the original instance and its neighbors. The amount of interpolation is controlled by a parameter called the sampling ratio. SMOTE can be applied to both numerical and categorical data. It is a popular technique in machine learning and data mining for improving the performance of classifiers on imbalanced datasets.

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SMOTE

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