What are some effective undersampling techniques in machine learning?

What are some effective undersampling techniques in machine learning?

Undersampling techniques help balance class distribution in datasets. Common methods include Random Undersampling, Tomek Links, and NearMiss.

Can you explain how Random Undersampling works?

Random Undersampling involves randomly removing samples from the majority class to achieve a balanced dataset, which can help improve model performance.

What are the pros and cons of using undersampling?

Pros include reduced training time and improved model accuracy on minority classes. Cons are potential loss of important data and underfitting.

Are there any alternatives to undersampling?

Yes, alternatives include oversampling techniques like SMOTE, or using ensemble methods that can handle imbalanced datasets.