Consider a scenario in which a user provides like$/$dislike ratings of a set of $20$ items,

in which she rates $9$ items as a $$like$$ and the remaining as a $$dislike$.$ Suppose

that $7$ item descriptions contain the word $$thriller$,$ and the user dislikes $5$ of these

items. Compute the Gini index with respect to the original data distribution, and with

respect to the subset of items containing the word $$thriller$.$ Should feature selection

algorithms retain this word in the item descriptions?

$2.$ Implement a rule$-$based classifier with the use of association pattern miningImplement a Bayes classifier with Laplacian smoothing.

$5.$ Repeat Exercise $3$ with the use of a Bayes classifier. Do not use Laplacian smoothing.

Explain why Laplacian smoothing is important in this case.

$6.$ Repeat Exercise $3$ with the use of a $1-$nearest neighbor classifier.