Data Science

Naive Bayes Classifier is a simple and intuitive method for the classification. The algorithm is based on Bayes’ Theorem with two assumptions on predictors: conditionally independent and equal importance. This technique mainly works on categorical response and explanatory variables. But it still can work on numeric explanatory variables as long as it can be transformed to categorical variables. This post is my note about Naive Bayes Classifier, a classification teachniques.

This post shows the R code for Naive Bayes Classifier by using funtion naiveBayes() in package e1071. And I use the simple example in my post, Naive Bayes Classifier, to show how to use this function. Details Resources for Package ‘e1071’ CRAN - Package ‘e1071’ Package ‘e1071’ - Reference manual Example Code (e1071::naiveBayes()) Suppose we have a contingency table like this: Q : And, what will be our guess on type if we have a data has X1=“Yes” and X2=“Unsure”?