Complete Tutorial on Naïve Bayes Classifier | Gaussian | Categorical | Bernoulli | Multinomial NB
Channel: DataTrek
Date: 2022-10-02

#datascience #machinelearning #artificialintelligence #analytics #statistics 


A naive Bayes classifier is a classification algorithm in machine learning included in supervised learning. Naive Bayes classifiers are a family of probabilistic classifiers based on applying Bayes' theorem with strong (naïve) independence assumptions, assuming that all predictors (or features) are independent, rarely happening in real life. That’s a very naïve assumption, giving it the name Naïve Bayes Classifier. It utilizes Bayes Theorem to classify data to any of the classes. The posterior probability of a data point belonging to a class is calculated using prior probability and likelihood, estimated over training-set. In this video, we will also cover variants of the Naive Bayes classifier which are Gaussian Naive Bayes, Categorical Naive Bayes, Bernoulli, and Multinomial Naive Bayes classifiers. We will also cover the concept of Adaptive Smoothing or Laplace smoothing.

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