What are some effective techniques for oversampling in machine learning?

What are some effective techniques for oversampling in machine learning?

Some popular techniques for oversampling include SMOTE (Synthetic Minority Over-sampling Technique), ADASYN, and random oversampling.

Can you explain how SMOTE works?

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

Are there any downsides to using oversampling techniques?

Yes, oversampling can lead to overfitting, as it may create duplicates or very similar samples, which can skew the model.

What is the difference between SMOTE and random oversampling?

Random oversampling simply duplicates existing minority samples, while SMOTE creates new synthetic samples, providing more diversity.