The five most common words appearing in spam emails are shipping!, today!, here!, available, and fingertips!. Many spam filters separate spam from ham (email not considered to be spam) through application of Bayes' theorem. Suppose that for one email account, 1 in every 10 messages is spam and the proportions of spam messages that have the five most common words in spam email are given below.
Homework Answers
a)P(Spam |shipping) =0.1*0.049/0.1*0.049+0.9*0.0013)=0.807
P(ham| |shipping) =1-0.807 =0.193
yes
b)P(Spam |today) =0.676
P(Spam |here) =0.604
today
because the probability is higher
c)
P(spam |available) =0.270
P(spam |fingertips) =0.546
fingertips
(please provide options for other parts)
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The five most common words appearing in spam emails are
shipping!, today!, here!,
available, and fingertips!....
The five most common words appearing in spam emails are shipping!, today!, here!, available, and fingertips!....
The five most common words appearing in spam emails are shipping!, today!, here!, available, and fingertips!. Many spam filters separate spam from ham (email not considered to be spam) through application of Bayes' theorem. Suppose that for one email account, 1 in every 10 messages is spam and the proportions of spam messages that have the five most common words in spam email are given below. shipping! today! here! available fingertips! 0.049 0.044 0.034 0.013 0.013 Also suppose that the...
Problem 5. (12 points) Bayes' Theorem is a popular tool for spam filtering. You are asked...
Problem 5. (12 points) Bayes' Theorem is a popular tool for spam filtering. You are asked to design a spam filtering algorithm based on whether certain words appear in an email. You were given 1,000 randomly selected emails that entered an email server. You examined these emails and manually labeled each one either as spam or ham (i.e., non-spam). You found that 400 emails are spam and 600 are ham. In these 400 spam emails, you found 200 of thenm...
Problem 5. (12 points) Bayes' Theorem is a popular tool for spam filtering. You are asked...
Problem 5. (12 points) Bayes' Theorem is a popular tool for spam filtering. You are asked to design a spam filtering algorithm based on whether certain words appear in an email. You were given 1,000 randomly selected emails that entered an email server. You examined these emails and manually labeled each one either as spam or ham (i.e., non-spam). You found that 400 emails are spam and 600 are ham. In these 400 spam emails, you found 200 of them...
Problem 1 (Bayes theorem and spam filters) Suppose you have develop a new algorithm to detect...
Problem 1 (Bayes theorem and spam filters) Suppose you have develop a new algorithm to detect spam in an incoming email message. if the email is spam, there is a 98% chance your algorithm will detect it. On the other hand, if no spam is present, there is a 90% chance the algorithm will indicate that the message is not spam. Suppose that roughly 10% of all your email is spam. a) What is the probability a randomly chosen message...
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IN JAVA PLS DUE TODAY Assignment 4 - Email, Shwitter and Inheritance Select one option from below. All (both) options are worth the same number of points. The more advanced option(s) are provided for students who find the basic one too easy and want more of a challenge. OPTION A (Basic): Message, EMail and Tweet Understand the Classes and Problem Every message contains some content ("The British are coming! The British are coming!"). We could enhance this by adding other...
The five most common words appearing in spam emails are shipping!, today!, here!, available, and fingertips!. Many spam filters separate spam from ham (email not considered to be spam) through application of Bayes' theorem. Suppose that for one email account, 1 in every 10 messages is spam and the proportions of spam messages that have the five most common words in spam email are given below. shipping! today! here! available fingertips! 0.049 0.044 0.034 0.013 0.013 Also suppose that the...
Problem 5. (12 points) Bayes' Theorem is a popular tool for spam filtering. You are asked to design a spam filtering algorithm based on whether certain words appear in an email. You were given 1,000 randomly selected emails that entered an email server. You examined these emails and manually labeled each one either as spam or ham (i.e., non-spam). You found that 400 emails are spam and 600 are ham. In these 400 spam emails, you found 200 of thenm...
Problem 5. (12 points) Bayes' Theorem is a popular tool for spam filtering. You are asked to design a spam filtering algorithm based on whether certain words appear in an email. You were given 1,000 randomly selected emails that entered an email server. You examined these emails and manually labeled each one either as spam or ham (i.e., non-spam). You found that 400 emails are spam and 600 are ham. In these 400 spam emails, you found 200 of them...
Problem 1 (Bayes theorem and spam filters) Suppose you have develop a new algorithm to detect spam in an incoming email message. if the email is spam, there is a 98% chance your algorithm will detect it. On the other hand, if no spam is present, there is a 90% chance the algorithm will indicate that the message is not spam. Suppose that roughly 10% of all your email is spam. a) What is the probability a randomly chosen message...
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