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.

Bu egzersiz

Fraud Detection in R

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

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.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Load ROSE
___

# Calculate the required number of cases in the over-sampled dataset
n_new <- ___

# Under-sample
undersampling_result <- ___(formula = ___, data = ___,
                           ___ = ___, ___ = ___, seed = 2018)

# Verify the Class-balance of the under-sampled dataset
undersampled_credit <- undersampling_result$___
___(___(___))

Kodu Düzenle ve Çalıştır