Conceptually with multiple examples. In this post, we are going to discuss the workings of Naive Bayes classifier conceptually so that it can later be applied to a real world dataset.
In the context of Supervised Learning (Classification), Naive Bayes or rather Bayesian Learning acts as a gold standard for evaluating other learning algorithms along with acting as a powerful probabilistic modelling technique.
In this post, we are going to discuss the workings of Naive Bayes classifier conceptually so that it can later be applied to a real world dataset.
In many applications the relationship between the attribute set and the class variable is non-deterministic. In other words, the class label of a test record cannot be predicted with certainty even though its attribute set is identical to some of the training examples. This situation may arise because of noisy data or the presence of certain confounding factors that affect classification.
For example, consider the task of predicting whether a person is at risk for heart disease based on the person’s diet and workout frequency. Although most people who eat healthily and exercise regularly have less chance of developing heart disease, they may still do so because of other factors such as heredity, excessive smoking, and alcohol abuse. Determining whether a person’s diet is healthy or the workout frequency is sufficient is also subject to interpretation, which in turn may introduce uncertainties into the learning problem.
Bayesian Learning is an approach for modelling probabilistic relationships between the attribute set and the class variable. In order to understand Naive Bayes classifier, the first thing that needs to be understood is Bayes Theorem.
Bayes theorem is derived from *Bayes Law *which states:
The probability of an event H given evidence E is:
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