What are some effective preprocessing techniques for imbalanced data?

What are some effective preprocessing techniques for imbalanced data?

Some effective techniques include resampling methods like oversampling the minority class, undersampling the majority class, and using synthetic data generation methods like SMOTE.

Can you explain how SMOTE works?

SMOTE, or Synthetic Minority Over-sampling Technique, creates synthetic examples of the minority class by interpolating between existing minority instances.

Are there any drawbacks to using SMOTE?

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

What other techniques can I use besides SMOTE?

You can also consider using ensemble methods like Random Forest or techniques like ADASYN, which adaptively generates synthetic data based on the density of minority class instances.