Machine Learning

Naive Bayes

A probabilistic classifier based on Bayes’ theorem and feature independence.

Naive Bayes calculates the probability that an example belongs to each class based on its observed features. It assumes that features are conditionally independent once the class is known.

Although this assumption is often unrealistic, the algorithm remains fast and effective for text classification, spam filtering and simple prediction tasks. It also works well with relatively small training datasets.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

JOIN NOW

Begin the first module

It is free, it is the real curriculum, and if it is not for you, you have lost nothing but an evening.

See the curriculum

Join any time · Build AI skills at your pace