The Cars Evaluation Data set consists of $7$ attributes, $6$ as feature attributes and $1$ as the target attribute $($car$\_$evaluation$).$ All the attributes are categorical.

$1$

buying

vhigh, high, med, low

$2$

maintenance

vhigh, high, med,low

$3$

doors

$2,3,4,5,$ more

$4$

persons

$2,4,$ more

$5$

lug$\_$boot

small, med, big

$6$

safety

low, med, high

$7$

car$\_$evaluation

unacc, acc, good, vgood

Questions

create a jupyter code to answer the following questions.

$1.$ Build decision tress from the Data using algorithms discussed in the class. Report model performance based on accuracy, error, sensitivity $($recall$),$ specificity, and precision using k$-$fold cross validation.

$2.$ Compare the models using ROC curves and Precision$-$Recall graphs

$3.$ Compute precision and recall using micro and macro averaging approaches. Interpret the results.