Undersampling Methods
Sometimes, you have so much data that adding more data by oversampling only makes things worse. Don’t worry, as we have a strategy for those situations as well. It’s called undersampling, or downsampling. In this chapter, you will learn about the concept of undersampling, including when to use it and the various techniques to perform it. You will also see how to use these techniques via the imbalanced-learn library APIs and compare their performance with some classical machine learning models.
In this chapter, we will cover the following topics:
- Introducing undersampling
- When to avoid undersampling in the majority class
- Removing examples uniformly
- Strategies for removing noisy observations
- Strategies for removing easy observations
By the end of this chapter, you’ll have mastered various undersampling techniques for imbalanced datasets and will be able to confidently apply them with the imbalanced-learn library...
