Step $2$: Classification Decision Tree

Using the code discussed in the lecture, split the data into training and testing data sets.

Use the rpart library to predict the variable TARGET$\_$BAD$\_$FLAG

Develop two decision trees, one using Gini and the other using Entropy using the training and testing data

All other parameters such as tree depth are up to you.

Do not use TARGET$\_$LOSS$\_$AMT to predict TARGET$\_$BAD$\_$FLAG.

Plot both decision trees

List the important variables for both trees

Using the training data set, create a ROC curve for both trees

Using the testing data set, create a ROC curve for both trees

Write a brief summary of the decision trees discussing whether or not the trees are are optimal, overfit, or underfit.

Rerun with different training and testing data at least three times.

Determine which of the two models performed better and why you believe this