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 tr$[$e statement setseed$(1)$ 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 four predictor variables to $x_4)$ and one binary target variable $(y).$ Follow the instructions below to create classification trees using the:Exercise $13,10$ 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$)?$

$\backslash$table$[[$Number of leaf nodes$],[$Predictor variable$],[$Split value$]]$