Naive Bayes

Naive Bayes is a family of probabilistic algorithms widely used for classification tasks in machine learning. Based on Bayes’ theorem, it operates under the assumption that features are conditionally independent given the class label. This simplicity allows Naive Bayes to perform well even with high-dimensional data and large datasets, making it particularly effective for text classification, such as spam detection and sentiment analysis. Despite its naive assumption, it often yields surprisingly accurate results, especially when the dataset is small. Its efficiency and ease of implementation make it a popular choice among data scientists and machine learning practitioners.

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...

Naive Bayes

Analytics Vidhya

Naive Bayes is a probabilistic machine learning algorithm. It is used widely to solve the classification problem. In addition to that this algorithm works perfectly in natural language problems…

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…

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…

(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.

Introduction to Naive Bayes for Machine Learning

Analytics Vidhya

Naive Bayes is a family of probabilistic algorithms that take advantage of probability theory and Bayes’ Theorem. They are probabilistic, which means that they calculate the probability of each tag…

Naive Bayes as explained through Ariana Grande lyrics

Analytics Vidhya

Naive Bayes is a machine learning classifier. More specifically it’s a probabilistic classifier, which means it predicts the probability that a feature is of each class, rather than just telling you…

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......

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…

Laplace smoothing in Naïve Bayes algorithm

Towards Data Science

Naïve Bayes is a probabilistic classifier based on Bayes theorem and is used for classification tasks. It works well enough in text classification problems such as spam filtering and the…