Naive Bayes is a probabilistic classification algorithm that applies Bayes' theorem with a strong independence assumption: it treats all features as conditionally independent given the class label. Its parameters are (1) a set of features assumed independent, (2) prior probabilities for each class, and (3) likelihood estimates from training data. It persists through computational libraries, academic curricula in machine learning, and deployed spam filters and text classifiers. [formal: naive bayes | substrate: mind | horizon: hours | explicit: yes | epoch: 0.15]
Accepted ontology entry
naive bayes
Naive Bayes is a probabilistic classification algorithm that applies Bayes' theorem with a strong independence assumption: it treats all features as conditionally independent given the class label. Its parameters are (1) a set of features…
Definition
Why it is in scope
A human-made probabilistic classification algorithm based on applying Bayes' theorem with strong independence assumptions between features. Despite its simplistic assumption of feature independence — which is rarely true in practice — it performs competitively on many real-world problems, particularly text classification, spam filtering, and document categorization. The algorithm estimates class probabilities from training frequency counts.
Names and aliases
- naive bayesen · CANONICAL
Relations from this entry
- cmsfrrt7e00ayqszgbbhie7ekDEPENDS_ON →
Naive Bayes is a classification algorithm that directly applies Bayes theorem with the naive independence assumption. Remove Bayes theorem as a concept and Naive Bayes cannot operate — its entire calculation (P(H|E) ∝ P(E|H)·P(H)/P(E)) is Bayes theorem. This is not meta-level; it's an object-level dependency.
- cmsfrrt7e00ayqszgbbhie7ekDERIVED_FROM →
Bayes theorem existed first and fed into naive bayes. Naive Bayes is Bayes theorem plus the independence assumption — historically the theorem preceded the algorithmic application.
- cmsm3b2kp00c51q13zu38aip0DERIVED_FROM →
Test direction: which existed first? Bayes' theorem (1763) and the general framework of bayesian inference predate the naive bayes classifier (1960s). The naive bayes method is a specific application that derives directly from bayesian inference principles, adding the independence assumption.
Relations to this entry
No accepted relations in this direction.
Record identity
- Created
- Aug 5, 2026, 7:37 AM UTC
- Content hash
- 6c64c5e4333e69f9f1d79729a58a267717087edbbee1b361c9c98e1f63533db2