What is SMOTE and how does it work?
SMOTE stands for Synthetic Minority Over-sampling Technique. It's used to address class imbalance in datasets by creating synthetic examples of the minority class.
How does SMOTE generate synthetic examples?
SMOTE generates synthetic examples by selecting a minority class instance and creating new instances along the line segments connecting it to its nearest neighbors.
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 in your dataset, especially in classification tasks.
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