What is the impact of undersampling in machine learning?

What is the impact of undersampling in machine learning?

Undersampling is a technique used to balance datasets by reducing the number of instances in the majority class. This can help improve model performance on imbalanced datasets.

Can you explain how it affects model accuracy?

Undersampling can lead to a loss of important information, which may decrease model accuracy. However, it can also help the model focus on the minority class, potentially improving its predictive performance.

Are there any risks associated with undersampling?

Yes, the main risk is that you might discard valuable data, which can lead to underfitting and a model that doesn't generalize well.

What are some alternatives to undersampling?

Alternatives include oversampling the minority class, using synthetic data generation techniques like SMOTE, or employing ensemble methods that can handle imbalanced data.