Chapter 4. Spam Filters, Naive Bayes, and Wrangling
The contributor for this chapter is Jake Hofman. Jake is at Microsoft Research after recently leaving Yahoo! Research. He got a PhD in physics at Columbia and regularly teaches a fantastic course on data-driven modeling at Columbia, as well as a newer course in computational social science.
As with our other presenters, we first took a look at Jake’s data science profile. It turns out he is an expert on a category that he added to the data science profile called “data wrangling.” He confessed that he doesn’t know if he spends so much time on it because he’s good at it or because he’s bad at it. (He’s good at it.)
Thought Experiment: Learning by Example
Let’s start by looking at a bunch of text shown in Figure 4-1, whose rows seem to contain the subject and first line of an email in an inbox.
You may notice that several of the rows of text look like spam.
How did you figure this out? Can you write code to automate the spam filter that your brain represents?
Figure 4-1. Suspiciously spammy
Rachel’s class had a few ideas about what things might be clear signs of spam:
Any email is spam if it contains Viagra references. That’s a good rule to start with, but as you’ve likely seen in your own email, people figured out this spam filter rule and got around it by modifying the spelling. (It’s sad that spammers are so smart and aren’t ...
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