(55 minutes to learn)
Naive Bayes is a modeling assumption used in classification, where we assume the observed data are conditionally independent given their class assignments. Despite its name, the standard naive Bayes model does not use Bayesian inference, but rather, a maximum likelihood estimation.
This concept has the prerequisites:
Core resources (read/watch one of the following)
→ Coursera: Machine Learning
An online machine learning course aimed at advanced undergraduates.
Location: Lecture "The naive Bayes classifier"
- Click on "Preview" to see the videos.
→ Stanford's Machine Learning lecture notes
Lecture notes for Stanford's machine learning course, aimed at graduate and advanced undergraduate students.
Location: Section 2 (Naive Bayes) of Part IV notes (Generative Learning algorithms) in Lecture Notes 2
→ The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
→ Bayesian Reasoning and Machine Learning
A textbook for a graudate machine learning course.
Supplemental resources (the following are optional, but you may find them useful)
- Naive Bayes is a generative model .
- Rather than performing a maximum likelihood estimation of the Naive Bayes model, we can use Bayesian inference to obtain posterior beliefs for the classes given the observed data.
- Here are some other commonly used classification algorithms: