$9$:$21$

$5$G

$72$

Exercise$-1$: Movie Review Sentiment Classification Using Na$$ve Bayes

Classify movie reviews as "positive" or "negative" based on features like the presence of certain keywords.

Dataset: Here is a small sample dataset:

$\backslash$table$[[$Review ID$,$Contains "Good",Contains "Bad",Contains "Great",Sentiment$],[1,$Yes,No$,$Yes,Positive$],[2,$No$,$Yes,No$,$Negative$],[3,$Yes,Yes,No$,$Negative$],[5,$No$,$No$,$Yes,Positive$],[5,$Yes,No$,$Yes,Positive$],[6,$No$,$Yes,No$,$Negative$]]$

Use Na$$ve Bayes algorithm to classify a new movie having features such as:

Contains "Good" $=$ Yes

Contains "Bad" $=$ No

Contains "Great" $=$ No

Exercise $-2$ Decision Tree $($ID$-3)$

Given the below dataset

We want to build a decision using the ID$-3$ algorithm.

$\backslash$table$[[$Age$,$Income,Student,Credit Rating,Buys Computer$],[30,$High,No$,$Fair,No$],[30,$High,No$,$Excellent,No$],[31-40,$High,No$,$Fair,Yes$],[>40,$Medium,No$,$Fair,Yes$],[>40,$Low,Yes,Fair,Yes$],[>40,$Low,Yes,Excellent,No$],[31-40,$Low,Yes,Excellent,Yes$],[30,$Medium,No$,$Fair,No$],[30,$Low,Yes,Fair,Yes$],[>40,$Medium,Yes,Fair,Yes$],[30,$Medium,Yes,Excellent,Yes$],[31-40,$Medium,No$,$Excellent,Yes$],[31-40,$High,Yes,Fair,Yes$],[>40,$Medium,No$,$Excellent,No$]]$

Questions:

$1-$ Calculate the Entropy of the Entire Dataset.

$2-$ Calculate the Information Gain for Each Attribute.

$3-$ Select the Attribute with the Highest Information Gain.

$4-$ Repeat the Process for Each Branch.

Ims.kku.edu.sa