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How does computer know what is spam and what is ham?

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Presentation on theme: "How does computer know what is spam and what is ham?"— Presentation transcript:

1 How does computer know what is spam and what is ham?

2 Attempt 1: (define (spam? email)‏ (cond ( (email from known sender) False)‏ ( (email contains “viagra”) True)‏ ( (email begins with “Dear Mr/Mrs.”) True)‏ ( (email contains URL) True)‏ ( (email contains attachment) True)‏ (...

3 Problem: (email contain URL) is an indication, NOT a PROOF Attempt 1: (define (spam? email)‏ (cond ( (email from known sender) False)‏ ( (email contains “viagra”) True)‏ ( (email begins with “Dear Mr/Mrs.”) True)‏ ( (email contains URL) True)‏ ( (email contains attachment) True)‏ (...

4 Features: Score: email from known sender -50 email contains "viagra" 75 email begins with "Dear Mr/Mrs." 70 email contains URL 10 email contains attachment 5......... If Total Sum > 100, classify as spam.

5 Features: Score: email from known sender -50 email contains "viagra" 75 email begins with "Dear Mr/Mrs." 70 email contains URL 10 email contains attachment 5......... If Total Sum > 100, classify as spam. Problems: - How to determine the score? - How to combine the score?

6 Key Idea: Learn which features are important through examples Training Set: lots of emails with correct labels (both spam and ham)

7 The Naive Bayes Algorithm: Step 1. Gather Statistics inside Training Set:

8 The Naive Bayes Algorithm: Step 1. Gather Statistics inside Training Set: - Count percentage of spams in Training Set: P(spam)‏ - Count percentage of hams in Training Set: P(ham)‏ - For every feature F_1, F_2, F_3... : = Count percentage of spams with feature F_i : P(F_i | spam)‏ = Count percentage of hams with feature F_i : P(F_i | ham)‏

9 The Naive Bayes Algorithm: Say, F_1 = email contains “viagra” F_2 = email begins with “Dear Mr/Mrs.”

10 The Naive Bayes Algorithm: Say, F_1 = email contains “viagra” F_2 = email begins with “Dear Mr/Mrs.” From Training Set, we discovered: P(spam) = 0.85 P(ham) = 0.15 P(F_1 | spam) = 0.2 P(NOT F_1 | spam) = 0.8 P(F_1 | ham) = 0.001 P(NOT F_1 | ham) 0.999 P(F_2 | spam) = 0.99 P(NOT F_2 | spam) = 0.01 P(F_2 | ham) = 0.0001 P(NOT F_2 | ham) = 0.9999

11

12 The Naive Bayes Algorithm: Step 1. Gather Statistics inside Training Set: - Count percentage of spams in Training Set: P(spam)‏ - Count percentage of hams in Training Set: P(ham)‏ - For every feature F_1, F_2, F_3... : = Count percentage of spams with feature F_i : P(F_i | spam)‏ = Count percentage of hams with feature F_i : P(F_i | ham)‏ Step 2. On a new Instance: - Find what features the new instance has - Use Bayes Rule to compute probability - Take the most probable label

13 Example: Optical Character Recognition GOAL: recognize scanned hand-written numbers..................................++++++......................##############++............+++++##########+..................+.+++++##+........................+##........................+##+.......................+##+........................+#+.........................##+........................+#+........................+##+........................##+........................###+.......................+##+.......................+##+.......................+###+.......................+##.......................................................+#........................+###.......................+####+......................+######+...................+###+####+..................+##..+####..................+#+...+##+..................+#+...###+..................+##+++####+..................#####++##+..................+###+..+##+..................+++....+#+.........................+##..........................+#+.........................+##+.........................+#+.........................+##+.........................+#+..........................+#+..........................#+................................

14 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares

15 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares........................................+#........................+###.......................+####+......................+######+...................+###+####+..................+##..+####..................+#+...+##+..................+#+...###+..................+##+++####+..................#####++##+..................+###+..+##+..................+++....+#+.........................+##..........................+#+.........................+##+.........................+#+.........................+##+.........................+#+..........................+#+..........................#+................................

16 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares........................................+#........................+###.......................+####+......................+######+...................+###+####+..................+##..+####..................+#+...+##+..................+#+...###+..................+##+++####+..................#####++##+..................+###+..+##+..................+++....+#+.........................+##..........................+#+.........................+##+.........................+#+.........................+##+.........................+#+..........................+#+..........................#+................................

17 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares........................................+#........................+###.......................+####+......................+######+...................+###+####+..................+##..+####..................+#+...+##+..................+#+...###+..................+##+++####+..................#####++##+..................+###+..+##+..................+++....+#+.........................+##..........................+#+.........................+##+.........................+#+.........................+##+.........................+#+..........................+#+..........................#+................................

18 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares........................................+#........................+###.......................+####+......................+######+...................+###+####+..................+##..+####..................+#+...+##+..................+#+...###+..................+##+++####+..................#####++##+..................+###+..+##+..................+++....+#+.........................+##..........................+#+.........................+##+.........................+#+.........................+##+.........................+#+..........................+#+..........................#+................................

19 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares........................................+#........................+###.......................+####+......................+######+...................+###+####+..................+##..+####..................+#+...+##+..................+#+...###+..................+##+++####+..................#####++##+..................+###+..+##+..................+++....+#+.........................+##..........................+#+.........................+##+.........................+#+.........................+##+.........................+#+..........................+#+..........................#+................................

20 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares Steps. - Turn image-file into a stream of Images (Abstract Data Type)‏ (done for you)‏

21 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares Steps. - Turn image-file into a stream of Images (Abstract Data Type)‏ (done for you)‏ - Gather feature statistics from Training File (mostly done for you)‏

22 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares Steps. - Turn image-file into a stream of Images (Abstract Data Type)‏ (done for you)‏ - Gather feature statistics from Training File (mostly done for you)‏ - Implement Bayes' Rule (mostly your own work)‏

23 Instance – scanned image of hand-written number Labels – 1,2,3,4,5,6,7,8,9 Features – (for project)‏ every 2x2 pixel squares Steps. - Turn image-file into a stream of Images (Abstract Data Type)‏ (done for you)‏ - Gather feature statistics from Training File (mostly done for you)‏ - Implement Bayes' Rule (mostly your own work)‏ - Evaluate your OCR by guessing labels on Validation File (mostly done for you)‏

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