Naive Bayes

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Naive Bayes

Naive Bayes is a family of probabilistic algorithms based on Bayes’ theorem, primarily used for classification tasks. The key feature of Naive Bayes is its “naive” assumption that all features are independent of each other given the class label. This simplification allows for efficient computation, making it particularly useful for large datasets and real-time applications.

There are several types of Naive Bayes classifiers, including Multinomial Naive Bayes, which is suitable for discrete features and often used in text classification; Bernoulli Naive Bayes, which is designed for binary features; and Gaussian Naive Bayes, which assumes that features follow a normal distribution. Each variant is tailored to specific types of data and classification problems ²⁵.

Despite its simplicity and the unrealistic independence assumption, Naive Bayes often performs surprisingly well in practice, especially in applications like spam detection and document classification ⁴⁵. It requires relatively little training data and is known for its speed and accuracy.

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

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...

All about Naive Bayes

Towards Data Science

Naive Bayes Data Science Machine learning Artificial Intelligence Deep learning neural networks algorithms data datasets

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......

The Not So Naive Bayes

Towards Data Science

Have you ever wondered how your email service provider classifies a mail as spam or not spam almost immediately after you have received it? Or have you thought how the recommendations by online…

Naive Bayes Explained

Towards Data Science

Machine learning approaches for classification can be discriminative and generative in nature. Basically, the distinction between one and the other lies in the process that is followed to obtain…

Naive Bayes Classifiers

Towards AI

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm, but a family of algorithms where all of them share a common principle…

Naïve Bayes Classifiers

Analytics Vidhya

Naive Bayes classifiers are a family of classifiers based in Bayes’ theorem that are quite similar to the linear models. However, they tend to be even faster in training. The price paid for this…

The Naive Bayes Classifier

Towards Data Science

The Naïve Bayes Classifier is perhaps the simplest machine learning classifier to build, train, and predict with. This post will show how and why it works. Part 1 reveals that the much-celebrated…

Naive Bayes Classification

Towards AI

Naive Bayes Classification is a supervised machine learning algorithm. It is one of the many algorithms that are derived from the Bayes’ theorem. The algorithm can be scaled as per requirement. The…

Naive Bayes in Machine Learning

Towards Data Science

Bayes’ theorem finds many uses in the probability theory and statistics. There’s a micro chance that you have never heard about this theorem in your life. Turns out that this theorem has found its…

The Naivety of Naive Bayes

Analytics Vidhya

What the developers wanted to achieve through Machine Learning was to enable computers to have minds of their own and learn to make decisions by themselves. Such an objective, as we know by the…