Exercise $13-9$ Algo $($Using R$)$

The answers to this problem are based on $R$ version $3\mathrm{.}5\mathrm{.}3.$ To replicate the results with newer versions of $R,$ execute the following

line of code at the beginning of the $R$ session or your $R$ code: suppressWarnings$($RNGversion$("3\mathrm{.}5\mathrm{.}3")).$

For R$,$ partition data sets into $70\%$ training and $30\%$ validation. Use the statement set.seed$(1)$ to specify the random seed of $1$ for both

data partitioning and cross$-$validation. If the predictor variable values are in the character format, then treat the predictor variable as a

categorical variable. Otherwise, treat the predictor variable as a numerical variable.

The accompanying data file contains five predictor variables $(x_1$ to $x_5)$ and one binary target variable $($y$).$ Follow the instructions below

to create classification trees using the Exercise $13\mathrm{.}9$ Data worksheet.

a$.$ Use the rpart function to build a default classification tree. How many leaf nodes are in the default classification tree? What are the

predictor variable and split value for the first split of the default classification tree $($root node$)?$