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Unformatted text preview: oo o o oo ooo oooo o ooo o oooo o o o o ooo o oo o oo o o o oo o o o o o o o oo o o oo oo o oo o oo oo o o o o o oo oooooooo o oo o ooo oo o oo oo o oo oo o o oo o oo o oo oo o ooooo ooo o o ooooo ooo ooo ooooooo oo o o oo oooooo o o o oo oo o o o oo o oo ooo ooo o ooooooooooo o ooooooo ooooooooo o oo o oo oooooo o ooo oooooooooooo o ooooooooo o o oooo o o oooo ooooo o ooooo oooooooooooo o o oo ooo o oooo ooooo o o ooooooooo o oo ooo oooo ooo oooooooooooo o oo oooooooooooo oo ooooooo o oooooooooo oooo o ooo oo oo oo o ooo o o oooo o o ooo oo o o oo ooo oooooo o oo o o o oo oooo o ooo o oooo o o oooooooooo o oo ooooooooo o oo oooooo oo o oo ooooo o o ooo oo o o oo oo ooooo o ooo o ooo oo o o o o ooooooooo ooooooooo o o o ooo o oo ooooooooo o o ooo o o oooooo o ooooo o ooooooo oo oo o o ooooooo o o oo ooooooo o oooooo o oo o oo oooooooo oo ooooo oo oo ooooo o ooooo o ooo o ooooo o ooo oo o oooo ooooooo o o oo ooooo ooo o o oo oo o oooo o oo ooo ooooooooo o oooooo o o oo o ooooo o oo o ooooooo ooo oooo oo oo o o ooooo oo ooo oo ooo o ooo o oooo oo ooo oo oo oooooo o o oo oo oooo ooo oooo o o ooooo oo o oo ooo o oo oo o oooo o o oo o o o oo o o o o o oo ooo ooooo oo o ooo oo ooooo o o ooo oooo oooooo oo o age 20 0 50 100 0.0 0.4 famhist 20 40 60 10 ESL Chapter 1 — Introduction Trevor Hastie and Rob Tibshirani Spam detection • data from 4601 emails sent to an individual (named George, at HP labs, before 2000). Each is labeled as spam or email. • goal: build a customized spam filter. • input features: relative frequencies of 57 of the most commonly occurring words and punctuation marks in these email messages. george you hp free ! edu remove spam 0.00 2.26 0.02 0.52 0.51 0.01 0.28 email 1.27 1.27 0.90 0.07 0.11 0.29 0.01 Average percentage of words or characters in an email message equal to the indicated word or character. We have chosen the words and characters showing the largest difference between spam and email. 11 ESL Chapter 1 — Introduction Trevor Hastie and Rob T...
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