Based on the results given, answer the questions below:

1. How often will this tree make a correct prediction (include decimals)? Provide your answer for both the training set and the validation set.

2. How likely is a customer to pay back their loan if they have two or more children, and make less than $43,951 in income?

(NOTE: When asked how likely cite the percentage!)

3. How likely is a customer to pay back their loan if they have a saving account and are married but have no child?

4. Describe the profile of the least likely customer to successfully repay their loan.

5. Describe the profile of the most likely customer to successfully repay their loan.

The HP SPLIT Procedure Classification Tree for payback Subtree Starting at Node =0 1 payback =02 payback =1 The HP SPLIT Procedure \begin{tabular}{|l|r|r|r|r|r|r|} \hline \multicolumn{7}{|c|}{ Variable Importance } \\ \hline \multirow{2}{*}{ Variable } & \multicolumn{2}{|c|}{ Training } & \multicolumn{2}{|c|}{ Validation } & Relative & \\ \cline { 2 - 8 } & Relative & Importance & Relative & Importance & Ratio & Count \\ \hline income & 0.9947 & 7.1734 & 1.0000 & 3.6881 & 1.0054 & 3 \\ \hline mortgage & 0.6428 & 4.6355 & 0.8179 & 3.0164 & 1.2725 & 2 \\ \hline children & 1.0000 & 7.2119 & 0.7632 & 2.8149 & 0.7632 & 3 \\ \hline save_act & 0.6159 & 4.4417 & 0.6297 & 2.3225 & 1.0225 & 2 \\ \hline married & 0.6727 & 4.8517 & 0.3314 & 1.2220 & 0.4925 & 1 \\ \hline \end{tabular} For this assignment, you'll be working with the bankloan.csv file, which has data about 600 customers that received personal loans from a bank. The president of the bank wants to predict how likely a future customer is to pay back their loan so she can make better loan approval decisions. The data file contains the following variahles: The HP SPLIT Procedure Classification Tree for payback Subtree Starting at Node =0 1 payback =02 payback =1 The HP SPLIT Procedure \begin{tabular}{|l|r|r|r|r|r|r|} \hline \multicolumn{7}{|c|}{ Variable Importance } \\ \hline \multirow{2}{*}{ Variable } & \multicolumn{2}{|c|}{ Training } & \multicolumn{2}{|c|}{ Validation } & Relative & \\ \cline { 2 - 8 } & Relative & Importance & Relative & Importance & Ratio & Count \\ \hline income & 0.9947 & 7.1734 & 1.0000 & 3.6881 & 1.0054 & 3 \\ \hline mortgage & 0.6428 & 4.6355 & 0.8179 & 3.0164 & 1.2725 & 2 \\ \hline children & 1.0000 & 7.2119 & 0.7632 & 2.8149 & 0.7632 & 3 \\ \hline save_act & 0.6159 & 4.4417 & 0.6297 & 2.3225 & 1.0225 & 2 \\ \hline married & 0.6727 & 4.8517 & 0.3314 & 1.2220 & 0.4925 & 1 \\ \hline \end{tabular} For this assignment, you'll be working with the bankloan.csv file, which has data about 600 customers that received personal loans from a bank. The president of the bank wants to predict how likely a future customer is to pay back their loan so she can make better loan approval decisions. The data file contains the following variahles