What are the best practices for using SMOTE in data preprocessing?

What are the best practices for using SMOTE in data preprocessing?

When using SMOTE, it's important to ensure that you apply it only to the training data to avoid data leakage.

Should I standardize my data before applying SMOTE?

Yes, standardizing your data before applying SMOTE can help improve the quality of the synthetic samples generated.

How do I choose the right k-neighbors for SMOTE?

Choosing the right k-neighbors often depends on your dataset; a common starting point is k=5, but you can experiment with different values.

Are there any alternatives to SMOTE?

Yes, alternatives include ADASYN, Borderline-SMOTE, and using ensemble methods like Random Forests.

Can SMOTE be used for multi-class classification?

Yes, SMOTE can be adapted for multi-class classification by applying it to each class separately.