What are some effective oversampling techniques for imbalanced datasets?

What are some effective oversampling techniques for imbalanced datasets?

Some effective oversampling techniques include SMOTE, ADASYN, and random oversampling. Would you like to know more about any of these?

Can you explain how SMOTE works?

SMOTE, or Synthetic Minority Over-sampling Technique, generates synthetic samples by interpolating between existing minority class samples. This helps to create a more balanced dataset.

What are the advantages of using ADASYN over SMOTE?

ADASYN focuses on generating more synthetic data for minority class samples that are harder to classify, which can improve model performance on difficult cases.

Are there any downsides to oversampling techniques?

Yes, oversampling can lead to overfitting, as it may create duplicates or very similar samples. It's important to validate the model properly.