Combining ROS & RUS

You can combine both random over-sampling (ROS) and random under-sampling (RUS) in order to balance the class distribution. You're going to re-balance the dataset such that the new dataset contains 10,000 transactions of which 30% are fraudulent.

Remember, you can always load ROSE in the console and enter ?ovun.sample to check which arguments the function takes.

Este exercício faz parte do curso

Fraud Detection in R

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Instruções do exercício

Load the ROSE package.
Set n_new equal to 10,000 and fraud_fraction to 30%.
Use both over and under-sampling.
Check the class-balance of the under-sampled dataset.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Load ROSE
___

# Specify the desired number of cases in the balanced dataset and the fraction of fraud cases
n_new <- ___
fraud_fraction <- ___

# Combine ROS & RUS!
sampling_result <- ___(___ = ___, ___ = ___,
                           ___ = ___, ___ = ___,  p = ___, seed = 2018)

# Verify the Class-balance of the re-balanced dataset
sampled_credit <- ___
prop.table(___(___))

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