What are some alternatives to Smote for handling imbalanced datasets?

What are some alternatives to Smote for handling imbalanced datasets?

There are several alternatives to Smote, such as ADASYN, Borderline-SMOTE, and Random Oversampling. Would you like to know more about any of these?

Can you explain how ADASYN works?

ADASYN, or Adaptive Synthetic Sampling, generates synthetic data points based on the density of minority class samples, focusing on harder-to-learn examples.

What about Borderline-SMOTE? How is it different?

Borderline-SMOTE focuses on generating synthetic samples near the decision boundary, which can improve the classifier's performance on difficult cases.

Are there any downsides to using these methods?

Yes, potential downsides include overfitting, increased computational cost, and the risk of generating noisy samples if not tuned properly.