12. Imagine that the proportion of all e-mails in our training data that are spam is .62, and the proportion of e-mails that are ham is .38. An e-mail comes through our Bayesian filter and it contains only two words: "lucrative lottery." From our training data, we know the probabilities that each of these words would appear in either type of e-mail. The probabilities are shown below: SPAM НАМ lucrative .05 .01 lottery .15 .03 Using a naive bayes methodology, what is the probability that this e-mail is spam?

A First Course in Probability (10th Edition)
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ISBN:9780134753119
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Chapter1: Combinatorial Analysis
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12. Imagine that the proportion of all e-mails in our training data that are spam is .62, and the
proportion of e-mails that are ham is .38. An e-mail comes through our Bayesian filter and it
contains only two words: "lucrative lottery." From our training data, we know the probabilities that
each of these words would appear in either type of e-mail. The probabilities are shown below:
SPAM
НАМ
lucrative
.05
.01
lottery
.15
.03
Using a naive bayes methodology, what is the probability that this e-mail is spam?
Transcribed Image Text:12. Imagine that the proportion of all e-mails in our training data that are spam is .62, and the proportion of e-mails that are ham is .38. An e-mail comes through our Bayesian filter and it contains only two words: "lucrative lottery." From our training data, we know the probabilities that each of these words would appear in either type of e-mail. The probabilities are shown below: SPAM НАМ lucrative .05 .01 lottery .15 .03 Using a naive bayes methodology, what is the probability that this e-mail is spam?
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