S$3$:Yes

S$4$:No

S$5$:Yes

S$6$: No

a$)$ Construct the confusion matrix to compare the predicted class labels with the actual class labels

for all samples.

b$)$ Calculate various evaluation metrics such as accuracy, precision, recall, and F$1-$score to assess

the performance of the classifier.

To construct a Receiver Operating Characteristic $($ROC$)$ curve, we need to calculate the True

Positive Rate $($TPR$)$ and False Positive Rate $($FPR$)$ at different cutoff points. The cutoff point

represents the threshold used to classify samples as positive or negative. Construct the ROC curve

on cutoff point $=0\mathrm{.}2$ and $0\mathrm{.}7.$Using Naive Bayesian classification method, predict a class label $($yes or no$)$ for the following

unknown's samples:

S$1$: $($Red Domestic SUV$)$

S$2$: $($Yellow Domestic SUV$)$

S$3$: $($Red Imported SUV$)$

S$4$: $($Red Domestic Sports$)$

S$5$: $($Yellow Domestic Sports$)$

S$6$: $($Yellow Imported SUV$)$

Show all computation steps.

The actual class labels for the unknown samples are as follows:

S$1$:Yes

S$2$: No