What are some use cases for SMOTE in data science?

What are some use cases for SMOTE in data science?

SMOTE, or Synthetic Minority Over-sampling Technique, is used to address class imbalance in datasets. Common use cases include fraud detection, medical diagnosis, and customer churn prediction.

Can you explain how SMOTE works?

Sure! SMOTE generates synthetic samples by interpolating between existing minority class instances, effectively increasing their representation.

What are the advantages of using SMOTE?

The main advantages include improved model performance, better generalization, and reduced bias towards the majority class.

Are there any limitations to using SMOTE?

Yes, SMOTE can lead to overfitting, especially if the synthetic samples are too similar to existing ones. It's also not suitable for all types of data.

What alternatives to SMOTE exist?

Alternatives include ADASYN, Random Oversampling, and using ensemble methods like Balanced Random Forest.