Part B$[5$ points$]$ : Build a decision tree classifier using the sklearn toolbox. Then compute metrics for performance like precision and recall. This is a binary classification problem, therefore we can label all points as either positive $($SPAM$)$ or negative $($NOT SPAM$).$

In $[67]$: def build$\_$dt$($data$\_$X$,$ data$\_$y$,$ max$\_$depth $=$ None, max$\_$leaf$\_$nodes $=$None$)$:

This function does the following:

$1.$ Builds the decision tree classifier using sklearn

$2.$ Fits it to the provided data.

Arguments

data$\_$X $-$ a np$.$ndarray

data$\_$y $-$ np$.$ndarray

max$\_$depth $-$ None if unrestricted, otherwise an integer for the maximum depth the tree can reach.

Returns:

A trained DecisionTreeClassifier

$\backslash$# your code here