1. Spam Filtering
The first and foremost application of Naive Bayes is its ability to classify texts and in particular, spam emails from non-spam ones.
It is one of the oldest spam filtering methodology, with the Naive Bayes spam filtering dating back to 1998. Naive Bayes takes two classes – Spam and Ham and classifies data accordingly.
2. Sentiment Analysis
It is a part of natural language processing that analyzes if the data is positive, negative or neutral. Another terminology for Sentiment Analysis is opinion mining. Using Naive Bayes, we can classify if the text is positive or negative or determine what class the sentiment of the person belongs to.
3. Recommendation Systems
Using Naive Bayes we can build recommendation systems. A recommendation system measures the likelihood of the person watching a film or not, given the past watches. It is also used in conjunction with collaborative filtering to filter information for the users.
4. Bayesian Neural Networks
Recently, Bayes’ Theorem has been extended into Deep Learning where it is used to design powerful Bayesian Networks. It is then used in complex machine learning tasks like stock forecasting, facial recognition etc. It is a currently trending topic and has revolutionized the field of deep learning.
Summary
In the end, we conclude that the use of Bayes’ Theorem is for finding the conditional probability of an event. It has several extensions that are used in Data Science such as Naive Bayes, Gaussian Naive Bayes’, Gaussian Neural Networks etc.
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