Synthetic Minority Oversampling Technique (SMOTE)

Synthetic Minority Oversampling Technique (SMOTE) is a method used in machine learning model training that synthesizes new examples of a minority class to compensate for a severe class imbalance in which there are too few minority class examples to effectively learn the decision boundary.

SMOTE References

• SMOTE: Synthetic Minority Over-sampling Technique

Broader Topics Related to SMOTE: Synthetic Minority Oversampling Technique

Machine Learning (ML)

Machine learning terms, processes, and methods