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# Naive Baye’s Algorithm

**[Medium](https://daily.dev/sources/medium_js)** · 5 min read · 0 upvotes · 0 comments

## Summary

An introductory explanation of the Naive Bayes classification algorithm covering probability basics, Bayes theorem, the naive independence assumption, and three variants: Bernoulli NB (binary features), Multinomial NB (text/word counts), and Gaussian NB (continuous data). Includes a practical sklearn workflow with code snippets for training, predicting, and evaluating a model, plus a comparison with Logistic Regression and guidance on when to use Naive Bayes.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@ankitamohanty1919191/naive-bayes-algorithm-7b55aec3e529>

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Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#nlp](https://daily.dev/tags/nlp), [#scikit](https://daily.dev/tags/scikit)

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