For example, in the case of a loan distribution, bank manager's identify customer’s occupation, income, age, location, previous loan history, transaction history, and credit score.

Features are those characteristics or attributes which affect the results of the label. Whenever you perform classification, the first step is to understand the problem and identify potential features and label. In this tutorial, you are going to learn about all of the following: It uses Bayes theorem of probability for prediction of unknown class. Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. Naive Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data. All the examples have the same kind of problem to classify reviews, loan applicants, and patients. Or As a loan manager, you want to identify which loan applicants are safe or risky? As a healthcare analyst, you want to predict which patients can suffer from diabetes disease. Suppose you are a product manager, you want to classify customer reviews in positive and negative classes.

Bayes test models