M06.3 CONNECT THE MECHANISM
A useful classifier with a strong independence assumption
In 2002 a simple word-counting trick started beating the spam flood. Build that filter yourself, and see why its "naive" assumption works better than it has any right to.
LESSON OVERVIEW10 min lesson
Lesson overview
In 2002 a simple word-counting trick started beating the spam flood. Build that filter yourself, and see why its "naive" assumption works better than it has any right to.
What you’ll explore
- Naive Bayes combines a class prior with feature likelihoods under conditional independence; smoothing and correlated features affect its probabilities and decisions.
GO TO THE SOURCE
Original explanations, connected to the research.
Deep Learning — probability and information theoryAn Introduction to Statistical Learning — authors’ materialsA Plan for Spam (Paul Graham, 2002)Suggest a correction
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