SMOTE, or Synthetic Minority Over-sampling Technique, is a powerful tool in the field of machine learning and data preprocessing. It addresses the issue of class imbalance by generating synthetic examples of the minority class, thereby balancing the dataset. This technique is particularly useful in cancer research where datasets often exhibit significant class imbalance due to the rarity of certain types of cancer compared to healthy controls.
