Naive Bayes From Scratch: A Spam Filter Built From Word Counts

Short summary

Naive Bayes powers spam filters by counting word frequencies and applying Bayes' rule—no gradient descent required. The naive assumption of word independence is a simplification, but practical tricks like Laplace smoothing and log-space arithmetic make the classifier effective. This tutorial includes a working implementation and interactive demo showing which words signal spam versus legitimate messages.

• •Simple classifier using word frequency counts and Bayes' rule—no training iterations needed
• •Handles numerical stability through Laplace smoothing and log-space arithmetic to prevent underflow
• •Interactive demo with working code shows real-time classification of spam signals

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