Atlas · GenAI 2026
Naive Bayes
Building probabilistic classifiers using Bayes’ theorem and conditional feature-independence assumptions, selecting a likelihood model suited to the feature representation.
Also searchable as: naive-bayes, Naive Bayes Classifier
conceptSupervised LearningAI consensus: 0/3
Prerequisites
- mediumProbability Theory
Conditional probability and Bayes’ theorem explain the estimator.
Recommended reference
scikit-learn: Naive Bayes — https://scikit-learn.org/stable/modules/naive_bayes.html
Reviewed sources
Primary and first-party material reviewed for this editorial summary. These citations are separate from the AI consensus score above.
- scikit-learn: Naive Bayes
Conditional independence, variant-specific feature distributions and probability-estimation limits.
- scikit-learn: GaussianNB
Gaussian feature likelihoods in Naive Bayes classification.
- scikit-learn: MultinomialNB
Count-feature likelihoods and additive smoothing for text classification.
Notes from AI deep research
Related skills
- → is subcategory of: Classification