What are some techniques to handle class imbalance in machine learning?

What are some techniques to handle class imbalance in machine learning?

There are several techniques to address class imbalance, including resampling methods, cost-sensitive learning, and using ensemble methods.

Can you explain resampling methods?

Sure! Resampling methods include oversampling the minority class, undersampling the majority class, or a combination of both.

What is SMOTE?

SMOTE stands for Synthetic Minority Over-sampling Technique. It generates synthetic samples for the minority class to balance the dataset.

Are there any drawbacks to using SMOTE?

Yes, SMOTE can lead to overfitting since it creates synthetic examples that may not represent real data well.

What about using ensemble methods?

Ensemble methods like Random Forest or AdaBoost can help improve performance on imbalanced datasets by combining multiple models.