Classification 

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

  • 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”?
    A : Our guess is Type B.

    #######################
#### Generate Data ####
#######################
X1=c(rep("yes",10),rep("no",40),rep("yes",70),rep("no",30))
X2=c(rep("yes",10),rep("no",10),rep("unsure",30),
     rep("yes",40),rep("no",50),rep("unsure",10))
train=data.frame(X1,X2, Type=c(rep("A",50),rep("B",100)))
head(train,3)
    ##    X1  X2 Type
## 1 yes yes    A
## 2 yes yes    A
## 3 yes yes    A
    test=data.frame(X1="yes",X2="unsure")
head(test)
    ##    X1     X2
## 1 yes unsure
    ################################
#### Naive Bayes Classifier ####
################################
if (!require("e1071")) install.packages("e1071")
library(e1071)
(m=naiveBayes(Type ~ ., data = train))
    ## 
## Naive Bayes Classifier for Discrete Predictors
## 
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
## 
## A-priori probabilities:
## Y
##         A         B 
## 0.3333333 0.6666667 
## 
## Conditional probabilities:
##    X1
## Y    no yes
##   A 0.8 0.2
##   B 0.3 0.7
## 
##    X2
## Y    no unsure yes
##   A 0.2    0.6 0.2
##   B 0.5    0.1 0.4
    (p=predict(m, test))
    ## factor(0)
## Levels: