What is SMOTE in machine learning?

What is SMOTE in machine learning?

SMOTE stands for Synthetic Minority Over-sampling Technique. It's used to address class imbalance in datasets.

How does SMOTE work to balance the classes?

SMOTE generates synthetic examples by interpolating between existing minority class instances, effectively increasing their number.

Are there any drawbacks to using SMOTE?

Yes, SMOTE can lead to overfitting since it creates synthetic data points that may not represent real-world variations.

When should I consider using SMOTE?

Consider using SMOTE when you have a significant class imbalance that affects the performance of your machine learning model.