What are some advanced techniques for handling imbalanced data in machine learning?

What are some advanced techniques for handling imbalanced data in machine learning?

Some advanced techniques include SMOTE, ADASYN, and ensemble methods like Balanced Random Forest.

Can you explain how SMOTE works?

SMOTE, or Synthetic Minority Over-sampling Technique, generates synthetic samples for the minority class by interpolating between existing samples.

What are the pros and cons of using ADASYN?

ADASYN adapts the number of synthetic samples based on the density of the minority class, which can improve model performance but may also introduce noise.

Are there any specific algorithms that work well with imbalanced data?

Yes, algorithms like XGBoost and Random Forest are often effective as they can handle class imbalance better than others.