What are some effective techniques for data balancing in machine learning?

What are some effective techniques for data balancing in machine learning?

There are several techniques for data balancing, including oversampling, undersampling, and using synthetic data generation methods like SMOTE.

Can you explain how SMOTE works?

SMOTE, or Synthetic Minority Over-sampling Technique, creates synthetic examples of the minority class by interpolating between existing examples.

What are the pros and cons of oversampling versus undersampling?

Oversampling can lead to overfitting, while undersampling may result in loss of important data. The choice depends on the dataset.

Are there any tools or libraries that can help with data balancing?

Yes, libraries like imbalanced-learn in Python provide various methods for data balancing, including SMOTE and random oversampling.