Problem 2: Spam Email Classification Sender Spam Link Urgency Domain Email Length Attachment Count Level Is Sample (Nominal) (Continuous) (Nominal) (Discrete) (Ordinal) Spam 1 example.com 1323 No 0 Low No 2 spammer.net 542 Yes 3 High Yes 3 legitimate.org 981 No 1 Medium No 4 example.com 698 Yes 5 High Yes 5 legitimate.org 1234 No 0 Low No 6 spammer.net 322 No 2 Medium Yes 7 example.com 657 No 1 Medium No 8 spammer.net 987 Yes 4 High Yes 9 legitimate.org 445 No 0 Low No 10 legitimate.org 1298 Yes 6 High Yes You are tasked with classifying emails as spam or not spam using Naive Bayes classification. You need to estimate the conditional probabilities for different attributes and the class variable "Is Spam." Exercises: 1. Calculate Prior Probabilities: Calculate the prior probabilities of an email being spam or not spam based on the provided dataset. 2. Conditional Probabilities for Nominal Attributes: Calculate the conditional probabilities of an email being spam or not spam for different sender domains (Nominal attribute). 3. Conditional Probabilities for Continuous Attributes: Calculate the conditional probabilities of an email being spam or not spam based on the email length (Continuous attribute). You can assume a Gaussian distribution. 4. Conditional Probabilities for Nominal Attributes with Multiple Categories: Calculate the conditional probabilities of an email being spam or not spam based on whether it has an attachment (Nominal attribute). 5. Conditional Probabilities for Discrete Attributes: Calculate the conditional probabilities of an email being spam or not spam based on the number of spam links (Discrete attribute). 6. Conditional Probabilities for Ordinal Attributes: Calculate the conditional probabilities of an email being spam or not spam based on the urgency level (Ordinal attribute). 7. Spam Classification: Given a new email with the following attributes, use Naive Bayes to classify it as spam or not spam: Sender Domain: "spammy.biz" Email Length: 765 Attachment: Yes Spam Link Count: 2 Urgency Level: Medium These exercises will help you practice estimating probabilities for a Naive Bayes classification problem with different types of attributes.

Problem 2: Spam Email Classification Sender Spam Link Urgency Domain Email Length Attachment Count Level Is Sample (Nominal) (Continuous) (Nominal) (Discrete) (Ordinal) Spam 1 example.com 1323 No 0 Low No 2 spammer.net 542 Yes 3 High Yes 3 legitimate.org 981 No 1 Medium No 4 example.com 698 Yes 5 High Yes 5 legitimate.org 1234 No 0 Low No 6 spammer.net 322 No 2 Medium Yes 7 example.com 657 No 1 Medium No 8 spammer.net 987 Yes 4 High Yes 9 legitimate.org 445 No 0 Low No 10 legitimate.org 1298 Yes 6 High Yes You are tasked with classifying emails as spam or not spam using Naive Bayes classification. You need to estimate the conditional probabilities for different attributes and the class variable "Is Spam." Exercises: 1. Calculate Prior Probabilities: Calculate the prior probabilities of an email being spam or not spam based on the provided dataset. 2. Conditional Probabilities for Nominal Attributes: Calculate the conditional probabilities of an email being spam or not spam for different sender domains (Nominal attribute). 3. Conditional Probabilities for Continuous Attributes: Calculate the conditional probabilities of an email being spam or not spam based on the email length (Continuous attribute). You can assume a Gaussian distribution. 4. Conditional Probabilities for Nominal Attributes with Multiple Categories: Calculate the conditional probabilities of an email being spam or not spam based on whether it has an attachment (Nominal attribute). 5. Conditional Probabilities for Discrete Attributes: Calculate the conditional probabilities of an email being spam or not spam based on the number of spam links (Discrete attribute). 6. Conditional Probabilities for Ordinal Attributes: Calculate the conditional probabilities of an email being spam or not spam based on the urgency level (Ordinal attribute). 7. Spam Classification: Given a new email with the following attributes, use Naive Bayes to classify it as spam or not spam: Sender Domain: "spammy.biz" Email Length: 765 Attachment: Yes Spam Link Count: 2 Urgency Level: Medium These exercises will help you practice estimating probabilities for a Naive Bayes classification problem with different types of attributes.

Fundamentals of Information Systems

8th Edition

ISBN:9781305082168

Author:Ralph Stair, George Reynolds

Publisher:Ralph Stair, George Reynolds

Chapter4: Telecommunications And Networks

Section: Chapter Questions

Problem 5SAT

See similar textbooks