Paper Contents
Abstract
The Naive Bayes classifier, a probabilistic machine learning technique, is useful for classification tasks. It is based on the Bayes theorem, which states that the likelihood of an event occurring given some observed evidence is equal to the prior probability of the event occurring. The Naive Bayes classifier can be trained on a dataset of labelled news articles, each of which is associated with a particular class or category, for the purpose of classifying news articles. The features of the articles, such as the words used and the length of the article, can then be used by the classifier to predict the class of an unseen article. The "naive" assumption, which is one of the key assumptions of the Naive Bayes classifier, is that the articlesfeatures are independent of one another. The classifier is able to predict outcomes without taking into account how features interact with one another because of this assumption. The Naive Bayes classifier can still perform well on many classification tasks, including the classification of news articles, despite this assumption.
Copyright
Copyright © 2024 M.snegha. This is an open access article distributed under the Creative Commons Attribution License.
