In this tutorial, we will learn our next machine-learning model called Naive Bayes. Naive Bayes is widely recognized as a sophisticated and effective tool known for its simplicity. Rooted in Bayesian probability theory, it has proven invaluable in diverse applications such as text classification, spam filtering, and medical diagnosis. Its strength lies in its capacity to make precise predictions with minimal computational complexity, making it a foundational concept in both introductory machine-learning courses and real-world applications.
Table of Contents
Prerequisites
- Linear Algebra
- Calculus
- Probability & Statistics
What You Will Learn:
- Concepts And Derivation
- Application
If you are familiar with Bayes theorem you can easily grasp the idea of Naive Bayes. It calculates the probability of class given the probability distribution of features but it makes very strong assumptions about the data that features are independent of each other that is why it is called “Naive”.
More formally, Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable. The different naive Bayes classifiers differ mainly by the assumptions they make regarding the probability distribution of features. It’s very easy to understand and fast. Before we go further, you should read this definition.
Naïve Bayes classifiers are a family of machine learning classification methods that use Bayes’ theorem to probabilistically categorize data. They are called naïve because they assume independence between the features. The main idea is to use Bayes’ theorem to determine the probability that a certain data point belongs in a certain class, given the features of that data. Despite what the name may suggest, the naïve Bayes classifier is not a Bayesian method.
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