Bayes' rule can be used to identify and filter spam emails and text messages.In this collection, 747 of the 5574 total messages (13.40%) are identified as spam. The word “free” is contained in 4.75% of all messages, and 3.57% of all messages both contain the word “free” and are marked as spam. The word “text” (or “txt”) is contained in 7.01% of all messages, and in 38.55% of all spam messages. Of all spam messages, 17.00% contain both the word “free” and the word “text” (or “txt”). Of all non-spam messages, 0.06% contain both the word “free” and the word “text” (or “txt”) . Given that a message contains the word “free” but does NOT contain the word “text” (or “txt”), what is the probability that it is spam?
Bayes' rule can be used to identify and filter spam emails and text messages.In this collection, 747 of the 5574 total messages (13.40%) are identified as spam. The word “free” is contained in 4.75% of all messages, and 3.57% of all messages both contain the word “free” and are marked as spam. The word “text” (or “txt”) is contained in 7.01% of all messages, and in 38.55% of all spam messages. Of all spam messages, 17.00% contain both the word “free” and the word “text” (or “txt”). Of all non-spam messages, 0.06% contain both the word “free” and the word “text” (or “txt”) . Given that a message contains the word “free” but does NOT contain the word “text” (or “txt”), what is the probability that it is spam?
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