Naive Bayes

Definition updated on November 2023

What is Naive Bayes?

The Naive-Bayes algorithm is a supervised learning method for classification issues that is based on the Bayes theorem. Simply expressed, a Naive Bayes classifier assumes that the existence of one feature in a class has no bearing on the presence of any more features. A well-liked supervised machine learning approach for classification applications like text classification is the Naive Bayes classifier. It is a member of the family of generative learning algorithms and simulates the input distribution for a particular class or category. This method is predicated on the idea that given the class, the properties of the input data are conditionally independent, enabling the algorithm to predict outcomes rapidly and precisely. Simple probabilistic classifiers that use Bayes' theorem are referred to as naive Bayes classifiers. Based on the information available and some prior knowledge, this theorem calculates the likelihood of a hypothesis. Predicting the class of the test data set is simple and quick. Additionally, multi-class prediction shows good results.

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