Naive Bayes Classifier Calculator

This calculator is designed for statisticians and data analysts who need to perform Bayesian classification. It helps you predict the probability of a class given certain features using the Naive Bayes Theorem.

Data Source and Methodology

All calculations are based strictly on Bayesian statistical methods. Please refer to the StatTrek Bayes Rule Calculator for detailed guidance.

The Formula Explained

The Naive Bayes Formula:

P(H|E) = \frac{P(E|H) * P(H)}{P(E)}

Glossary of Terms

  • Prior Probability (P(H)): The initial probability of hypothesis H being true.
  • Likelihood (P(E|H)): The probability of evidence E given that H is true.
  • Evidence (P(E)): The total probability of observing the evidence.

Practical Example

Imagine you want to classify emails as spam or not spam. You have a prior probability of spam emails, the likelihood of certain words given spam classification, and the overall frequency of these words.

Frequently Asked Questions (FAQ)

What is Naive Bayes?

Naive Bayes is a classification technique based on Bayes' Theorem with the assumption of independence among predictors.

How is it used?

It's widely used for text classification and spam filtering.

What are the types of Naive Bayes?

Common types include Gaussian, Multinomial, and Bernoulli Naive Bayes.

Tool developed by Ugo Candido.
Last reviewed for accuracy on: October 2023.