Principles of Data Science
SMOTE, or Synthetic Minority Over-sampling Technique, is an advanced technique used to address class imbalance in datasets by generating synthetic examples of the minority class. This method enhances the learning capabilities of machine learning algorithms, making them more effective when dealing with imbalanced datasets. By creating new data points that are a combination of existing minority class instances, SMOTE helps improve model performance and reduce bias.
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