Classification

LDA and QDA are classification methods based on the concept of Bayes’ Theorem with assumption on conditional Multivariate Normal Distribution. And, because of this assumption, LDA and QDA can only be used when all explanotary variables are numeric. This post is my note about LDA and QDA, classification teachniques. All the contents in this post are based on my reading on many resources which are listed in the References part.

This post shows the R code for LDA and QDA by using funtion lda() and qda() in package MASS. To show how to use these function, I created a function, bvn(), to generate bivariate normal dataset based on the assumptions and then used lda() and qda() on the generated datasets. Details Resources for Package ‘MASS’ CRAN - Package ‘MASS’ Package ‘MASS’ - Reference manual Example Code LDA : Suppose our dataset are from \[X_{green}=N(\begin{pmatrix} 0.

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”?