(b) P (BIA) (c) P (AIB) 2-179. An e-mail filter is planned to separate valid e-mails from spam. The word free occurs in 60% of the spam mes sages and only 4% of the valid messages. Also, 20% of the messages are spam. Determine the following probabilities: (a) The message contains free. (b) The message is spam given that it contains free. (c) The message is valid given that it does not contain free.
Added by Douglas R.
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6 (probability of "free" given spam) - P(F|A) = 0.04 (probability of "free" given valid) - P(S) = 0.2 (probability of spam) - P(A) = 0.8 (probability of valid) Use the law of total probability: P(F) = P(F|S) * P(S) + P(F|A) * P(A) P(F) = 0.6 * 0.2 + 0.04 * Show more…
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An e-mail filter is planned to separate valid e-mails from spam. The word free occurs in $60 \%$ of the spam messages and only $4 \%$ of the valid messages. Also, $20 \%$ of the messages are spam. Determine the following probabilities: a. The message contains free. b. The message is spam given that it contains free. c. The message is valid given that it does not contain free.
Narayan H.
A spam filter is designed by looking at commonly used phrases in spam e-mails. Suppose that 55% of e-mails received in a particular company are spam. In 70% of the spam e-mails, the phrase "free money" is used, whereas this phrase is only used in 10% of the non-spam e-mails. A new e-mail has just arrived. (a) What is the probability that this e-mail is not spam? (b) What is the probability that this e-mail mentions the phrase "free money"? (c) Given that this new e-mail mentions the phrase "free money", what is the probability that it is indeed spam?
Sheryl E.
Suppose that a Bayesian spam filter is trained on a set of 500 spam messages and 200 messages that are not spam. The word “exciting” appears in 40 spam messages and in 25 messages that are not spam. Would an incoming message be rejected as spam if it contains the word “exciting” and the threshold for rejecting spam is 0.9?
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