How do you simultaneously undersample and oversample?

1. How do you oversample and Undersample?
2. Which among the following can be used to combine both undersampling and oversampling to handle an imbalanced class problem?
3. What is the effect of undersampling or oversampling?

How do you oversample and Undersample?

Random oversampling involves randomly selecting examples from the minority class, with replacement, and adding them to the training dataset. Random undersampling involves randomly selecting examples from the majority class and deleting them from the training dataset.

Which among the following can be used to combine both undersampling and oversampling to handle an imbalanced class problem?

Manually Combine Random Oversampling and Undersampling

A good starting point for combining resampling techniques is to start with random or naive methods. Although they are simple, and often ineffective when applied in isolation, they can be effective when combined.

What is the effect of undersampling or oversampling?

In both over sampling and under sampling, simple data duplication is rarely suggested. Generally, over sampling is preferable as under sampling can result in the loss of important data.