
Show HN: Naive Bayes classifier for text categorization in five steps - gchavez2
https://towardsdatascience.com/implementing-a-naive-bayes-classifier-for-text-categorization-in-five-steps-f9192cdd54c3
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jgrahamc
This is not a bad explanation but when doing this practically it can be useful
to take log() of the probabilities so that you work with sums of logs rather
than multiplying small floats.

[http://getpopfile.org/docs/faq:bayesandlogs](http://getpopfile.org/docs/faq:bayesandlogs)

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gchavez2
Thank you for the insight John, I have included your remark on the article.

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ColinWright
From the article:

    
    
        For an English spam classifier that
        considers all the words in the English
        language, the number of the words (n)
        is approximately 171,476.
    

That's a remarkably precise number to be preceded by the word "approximately".

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gchavez2
Agree, that was odd, it now reads:

"the number of the words (n) is approximately 170k"

Thank you for the remark.

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atum47
Nice article, very glad to read it. Keep up the good work.

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gchavez2
Thank you Victor, I enjoyed your JS articles too!

