Naive bayes classifier

As explained in Natural Language Processing in Action, Second Edition

Naive Bayes is a family of probabilistic classifiers based on Bayes' theorem and a conditional-independence assumption among features.

How Natural Language Processing in Action, Second Edition covers it

How deep it goes

Builds a runnable bag-of-words classifier and evaluates predictions, but does not derive Bayes' theorem or classifier internals.

Examples

Demonstrates movie-review and product-review datasets, token-count tables, probability predictions, and sentiment accuracy measurements.

What it assumes

Assumes labeled text data and basic tabular processing while demonstrating tokenization and binary-label construction.

What you can do after

Builds a MultinomialNB sentiment classifier and diagnoses its weaker performance when applied to product reviews.

How it connects

Connects Bayes' theorem and conditional independence with tokenization, bag-of-words representations, sentiment analysis, and dimensionality reduction.

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Also explained in

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  Grokking Machine LearningA probabilistic classification model that estimates class probabilities from observed features using the naive Bayes assumption.
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  Causal AIA naive Bayes classifier is a probabilistic classifier that applies Bayes’ rule while assuming conditional independence among features given the class.
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  Machine Learning Algorithms in DepthA probabilistic classifier that assumes features are conditionally independent given the class label.
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  LLMs in ProductionA naive Bayes classifier is a probabilistic classification model that applies Bayes' theorem while assuming conditional independence among features given the class.

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Related concepts

• Bayes' theoremuses · Bayes' theorem is a probability rule that expresses a conditional probability in terms of a prior probability, a likelihood, and the evidence.
• bayesian statisticsuses · Bayesian statistics is a framework for statistical inference that combines prior probability distributions with observed-data likelihoods to update probabilities over hypotheses or unknown quantities.
• probability theoryproduces · Probability theory is the branch of mathematics that formalizes uncertainty and analyzes random events and outcomes using probability measures.
• Bernoulli naive Bayesincludes · Bernoulli naive Bayes is a probabilistic classifier that models binary feature occurrences using Bernoulli likelihoods and assumes conditional independence given the class.
• conditional independenceuses · Two variables are conditionally independent given specified information when their conditional joint distribution factors into the product of their conditional distributions.

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.