Naive Bayes Classification using Scikit-learn

Learn how to build and evaluate a Naive Bayes Classifier using Python’s Scikit-learn package.

Suppose you are a product manager, you want to classify customer reviews in positive and negative classes. 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. All the examples have the same kind of problem to classify reviews, loan applicants, and patients.

Naive Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data. Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class.

In this tutorial, you are going to learn about all of the following:

• Classification Workflow
• What is the Naive Bayes classifier?
• How Naive Bayes classifier works?
• Classifier building in Scikit-learn
• Zero Probability Problem
• Its advantages and disadvantages

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Classification Workflow

Whenever you perform classification, the first step is to understand the problem and…