Exercise

Shrinking the majority group

Rather than increasing the number of fraud cases in the dataset, you can randomly remove legitimate cases to balance the dataset. Let's under-sample the majority class (Class = 0) in the creditcard dataset. You can use table() in the console to know how many fraudulent and legitimate transactions there are in the dataset.

Instructions

100 XP

Load the ROSE library.
Specify n_new as the required number of cases in the under-sampled dataset such that the new dataset will consist of 40% fraud cases. For this, you have to divide the number of fraud cases by the desired percentage of fraud cases in the under-sampled dataset.
Under-sample the dataset.
Use table() and prop.table() to check the class-balance of the under-sampled dataset.