Naive Bayes

Naive-Bayes

Naive Bayes is a family of probabilistic algorithms based on Bayes’ Theorem, primarily used for classification tasks in machine learning. It operates under the assumption that all features are independent of each other, which simplifies the computation of probabilities. Despite this “naive” assumption, Naive Bayes classifiers often perform surprisingly well in practice, especially in text classification tasks such as spam detection and sentiment analysis. There are several types of Naive Bayes classifiers, including Multinomial, Bernoulli, and Gaussian, each suited for different types of data and applications. Its simplicity and efficiency make it a popular choice for many machine learning projects.

What is Naive Bayes?

Becoming Human: Artificial Intelligence Magazine

An introduction to machine learning algorithms Naive Bayes algorithm is a supervised learning algorithm(probabilistic machine learning algorithm), which is based on Bayes theorem, used in a wide vari...

Codecademy

Naive Bayes algorithms are supervised learning methods that calculate the probability of an outcome based on input data and prior knowledge, assuming all inputs are equal and independent of each other...

Naive Bayes Explained

Towards Data Science

Naive Bayes is a probabilistic algorithm that’s typically used for classification problems. Naive Bayes is simple, intuitive, and yet performs surprisingly well in many cases. For example, spam…

(Gaussian) Naive Bayes

Towards Data Science

Naive Bayes is a widely used model in machine learning. Click here to learn more about the theory behind it, and how to implement it in Python.

Naïve Bayes Algorithm

Analytics Vidhya

Naive Bayes is a classification technique that is based on Bayes’ Theorem with an assumption that all the features that predicts the target value are independent of each other. It calculates the…

What’s so naive about naive Bayes’?

Towards Data Science

Naive Bayes (NB) is ‘naive’ because it makes the assumption that features of a measurement are independent of each other. This is naive because it is (almost) never true. Here is why NB works anyway…

1.9. Naive Bayes

Scikit-learn User Guide

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the val......

Naive Bayes Classification

Analytics Vidhya

Naive Bayes is a supervised learning classification. It is a probabilistic classifier based on Bayes theorem. The name naive stems from the fact that classifier assumes that pairs of features are…

Naive Bayes: Intuition and Implementation

Towards Data Science

In a broad sense, Naive Bayes models are a special kind of classification machine learning algorithms. They are based on a statistical classification technique called ‘Bayes Theorem’. Naive Bayes…

Understanding Naïve Bayes algorithm

Towards Data Science

Naïve Bayes is a classification algorithm that is a probabilistic classifier based on Bayes theorem. Before getting into the intricacies of Naïve Bayes, we first understand the Bayes theorem. Bayes…

AI Anyone Can Understand: Part 10 — Naive Bayes

Towards AI

Naive Bayes is a way for computers to learn how to make predictions based on data. Imagine you are trying to guess what a toy is just by looking at it. You might look at different parts of the toy…

A Machine Learning Roadmap to Naive Bayes

Analytics Vidhya

Naive Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this article, we shall be understanding the Naive Bayes…