Question $1(2\mathrm{.}5$ pts$)$: The following two confusion matrices represent the per$-$

formance of two different classifiers, C $1$ and C $2,$ on the same validation dataset

$($which had $100$ data points$).$ Both classifiers were built to predict whether the

person is likely to buy a luxury car. Compare the two classifiers based on their

predictive accuracy as well as precision, recall, and F$-$measure $($for class

"Yes", i$.$e$.,$ for the purchase outcome$).$ Show the calculation for each metric

$($i$.$e$.,$ don't just report which classifier has higher performance$).$

Also, compute the accuracy of the na$$ve $($majority$)$ rule on this validation

dataset. Hint: you may want to first draw the confusion matrix that you would

get with na$$ve$/$majority rule, to help you with accuracy calculation.

Classifier C$1$:

Classifier C$2$:

Also, compute the accuracy of the na$$ve $($majority$)$ rule on this validation dataset. Hint: you may want to first draw the confusion matrix that you would get with na$$ve$/$majority rule, to help you with accuracy calculation.