What is random oversampling?

Can you explain what random oversampling is?

Sure! Random oversampling is a technique used to address class imbalance in datasets by duplicating examples from the minority class.

Why is random oversampling important in machine learning?

It's important because it helps improve the model's performance by providing it with more examples of the minority class, which can lead to better predictions.

Are there any downsides to using random oversampling?

Yes, one downside is that it can lead to overfitting since it duplicates existing data points, which may not add new information.

What are some alternatives to random oversampling?

Alternatives include random undersampling, SMOTE (Synthetic Minority Over-sampling Technique), and using ensemble methods.