Naive Bayes Classifier

Naive Bayes Classifier Naive Bayes models are extremely fast, very simple and highly suitable for high dimensional datasets. The reason behind this is there simplicity and very less tunable parameters. This tutorial will cover the following sub topics and explain everything with a suitable and understandable examples. if you are a beginner this tutorial is for you. You can check the list of topics given below and start from where ever you want.  In this tutorial we have set following few agendas to discuss.  a) Bayesian Theorem b) Proof of Bayes Theorem c) Example on Conditional Probability c)  Bayesian Classification d) Weather Dataset Example e)  Python code to understand Gaussian Naive Bayes classifier f) Python  Coding Exercise on Iris Data Set g) When should you use Naive Bayes Classifier Bayesian Theorem This is one of the most important theorems in the theory of probability and statistics. It describes the relationship of conditional probabilities of stat...