Naives Bayes Classifier

Naive Bayes is the method of supervised learning. It is a classification algorithm which is used to predict the class of  a given data points. Naive Bayes is based on Bayes Theorem in which the probability of event which is based on previous knowledge and event.

Example: If the vehicles is long width and high height then it is a truck or if it has short width and height then it can be classified has a car.

The existence of these features are dependent of other features. However, these features can contributes to the probabilities of vehicles independently.

Bayes theorem is calculating of posterior probability P(c|x) which is equal to
P(x|c).P(c)/ P(x).

Where P(x|c) is the likelihood, P(c) is the prior probability of and P(x) is the prior probability of features.

By the help of Machine Learning Mastery I had written simple Gaussian Naive Bayes classifier using probability distributive function. Code can be found here. I have used Abalone data However, sklearn library provides the great implementation of different type of Naive Bayes classifier.

Other type of Naive Bayes classifier can be

  1. Multinomial Naive Bayes
  2. Bernoulli Naive Bayes

Application of Naive Bayes

These simple classification can come in handy when building a classification applications. Following can be possible applications build using Naive Bayes classification

  1. Recommendation System
  2. Real time predication
  3. Spam Filtering
  4. Text classifications
  5. Sentiment analysis

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