What is the difference between oversampling and undersampling in data processing?

What is the difference between oversampling and undersampling in data processing?

Oversampling involves increasing the number of instances in the minority class, while undersampling reduces the majority class instances.

Why would I choose to use oversampling instead of undersampling?

Oversampling can help retain valuable information from the minority class, which might be lost during undersampling.

Are there any techniques for oversampling?

Yes, techniques like SMOTE (Synthetic Minority Over-sampling Technique) and ADASYN are commonly used for oversampling.

What are the risks of using undersampling?

Undersampling can lead to loss of important data, which may result in a less accurate model.

Can you give me an example of when to use each method?

Use oversampling when the minority class is crucial for your analysis, and undersampling when you have a large majority class that can be reduced without losing significant information.