Naive Bayes Classification Algorithm

You are a data scientist,  an Intelligence Artificielle engineer, or a Statisticien, this post can help you to resolve your problems

You are working on a classification problem and you have generated your set of hypothesis, created features and discussed the importance of variables. Within an hour, stakeholders want to see the first cut of the model.

What will you do? You have hunderds of thousands of data points and quite a few variables in your training data set. In such situation, if I were at your place, I would have used ‘Naive Bayes‘, which can be extremely fast relative to other classification algorithms. It works on Bayes theorem of probability to predict the class of unknown data set.

You wold like to know haw it work ?

Step 1: Convert the data set into a frequency table

Step 2: Create Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64.

Step 3: Naive Bayesian equation to calculate the posterior probability for each class. The class with the highest posterior probability is the outcome of prediction.


Applications : 

Real time prediction , Multi Class Prediction, Text classification ,Spam Filtering, Sentiment Analysis ,Recommendation System...

My NuGet Package ici : 

Pm> Install-Package NaiveBayes.Classification -Version 1.0.0