Smote, or Synthetic Minority Over-sampling Technique, is a statistical technique used to address class imbalance in datasets by generating synthetic samples of the minority class. This method helps improve the performance of machine learning models by balancing the representation of different classes, thus providing a more accurate understanding of the data. Smote works by interpolating between existing minority instances to create new, similar examples, enhancing the training dataset.
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