Step $1$: Read in the Data

Read the data into R

List the structure of the data $($str$)$

Execute a summary of the data

Print the first six records

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

Step $3$: Regression 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$\_$LOSS$\_$AMT

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

Develop two decision trees, one using anova and the other using poisson

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

Plot both decision trees

List the important variables for both trees

Using the training data set, calculate the Root Mean Square Error $($RMSE$)$ for both trees

Using the testing data set, calculate the Root Mean Square Error $($RMSE$)$ 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

Step $4$: Probability $/$ Severity Model Decision Tree $($Push Yourself!$)$

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

Use the rpart library to predict the variable TARGET$\_$LOSS$\_$AMT using only records where TARGET$\_$BAD$\_$FLAG is $1.$

Plot both decision trees

List the important variables for both trees

Using your models, predict the probability of default and the loss given default.

Multiply the two values together for each record.

Calculate the RMSE value for the Probability $/$ Severity model.

Rerun at least three times to be assured that the model is optimal and not over fit or under fit.

Comment on how this model compares to using the model from Step $3.$ Which one would your recommend using?