Construct a decision list to classify the data below. Select tests to be as small as possibleConstruct a decision list to classify the data below. Select tests to be as small as possible

$($in terms of attributes$),$ breaking ties among tests with the same number of attributes by

selecting the one that classifies the greatest number of examples correctly. If multiple tests

have the same number of attributes and classify the same number of examples, then break the

tie using attributes with lower index numbers $($e$.$g$.,$ select $A_1$ over $A_2).$

$(10$ points$)$

function LEARN$-$DECISION$-$TREE$($examples$,$ attributes, parent$\_$examples$)$ returns a tree

if examples is empty then return PLURALITY$-$VALUE$($parent examples$)$

else if all examples have the same classification then return the classification

else if attributes is empty then return PLURALITY$-$VALUE$($examples$)$

else

$,$ examples

$($in terms of attributes$),$ breaking ties among tests with the same number of attributes by

selecting the one that classifies the greatest number of examples correctly. If multiple tests

have the same number of attributes and classify the same number of examples, then break the

tie using attributes with lower index numbers $($e$.$g$.,$ select $A_1$ over $A_2).$