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Machine Learning in JavaScript
This document discusses machine learning in JavaScript and how it can be used to automatically analyze content on the web. It provides an overview of naive Bayes classification and how words in documents can be counted and compared to known spam or non-spam documents to determine if new documents are spam or not. Examples are given of how naive Bayes could be used to build a spam filter as well as how the technique could potentially be applied to analyze content on sites like Hacker News and surface the most valuable posts.
Machine Learning in JavaScript
- 1. Machine Learning in JavaScript
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- 6. Two years •Secret sauceat SpotterRF •StackOverflow.com •Random side projects
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- 15. “Making computers modifyor adapt their actions so these actions get more accurate.” - Stephen Marsland
- 16. Teaching computers to recognize patterns
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- 20. An avalanche of data
- 21. “The purpose of computingis insight, not numbers.” - Richard Hamming
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- 26. Naive Bayes and puppies
- 27. Spam Filtering? Illustrious sir/madame, Ihave recently acquired a bounteous cache of 7 million semicolons. They can all be yours if you send a money order for 30 semicolons. Most graciously, A spammer
- 28. An idea • Countup all words in all spam • Count up all words in not-spam • Compare counts to words in new documents
- 29. Class Word 1 Word 2 Word 3 Word 4
- 30. Spam / NotSpam Word 1 Word 2 Word 3 Word 4
- 31. Spam / NotSpam Word 1 Word 2 Word 3 Word 4 Spam Not 50 45
- 32. Spam / NotSpam Word 1 Word 2 Word 3 Word 4 Spam Not 15 27
- 33. Spam / NotSpam Word 1 Word 2 Word 3 Word 4 Spam Not 33 14
- 34. Spam / NotSpam Word 1 Word 2 Word 3 Word 4 Spam Not 4 55
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- 39. Bayes Theorem P(A|B) =(P(B|A)*P(A)) / P(B)
- 40. In English P(class|email) =(P(email|class)*P(class)) / P(word1 and word2 and word3)
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- 42. “Spam” or “NotSpam” P(class|email) = (P(email|class)*P(class))/P(email)
- 43. Words in theemail P(class|email) = (P(email|class)*P(class))/P(email)
- 44. What we thinkthe probability of spam or not spam is P(class|email) = (P(email|class)*P(class))/P(email)
- 45. P(spam|email) = (P(email|spam)*P(spam))/P(email) P(not spam|email) = (P(email|not spam)*P(not spam))/P(email) Pick the largest one
- 46. P(spam|email) = (P(email|spam)*P(spam))/P(email) P(not spam|email) = (P(email|not spam)*P(not spam))/P(email) These are the same
- 47. P(spam|email) = (P(email|spam)*P(spam)) P(notspam|email) = (P(email|not spam)* P(not spam)) Assume these are the same
- 48. P(spam|email) = P(email|spam) P(notspam|email) = P(email|not spam)
- 49. P(words|spam) = P(word1|spam)* P(word2|spam) . . . * P(word_n|spam)
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- 65. Crap Crap Crap
- 66. Crap Crap Crap Crap
- 67. Crap Crap Crap Crap Crap
- 68. Crap Crap Crap Crap Crap Crap
- 69. Crap Crap Crap Crap Crap Crap Crap
- 70. Crap Crap Crap Crap Crap Crap Crap Crap
- 71. Crap Crap Crap Crap Crap Crap Crap Crap Crap
- 72. Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap
- 73. Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap
- 74. Mostly Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap Crap
- 75. Yehuda Katz hurt myfeelings rails sucks node rules lol
- 76. Yehuda Katz hurt myfeelings DRAMA rails sucks node rules lol
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- 83. Automatically Find TheGood Stuff
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- 85. scraping with jsdom
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- 88. 90% data prep 10% learning
- 89. naive bayes with credulous
- 90. Like / Dislike Word1 . . . Word n username hostname
- 91. programmer ui with node
- 92. recommend posts from HN api
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