This document demonstrates using naive Bayes classification to analyze two datasets - contacts and iris data. For each dataset, the data is split into a training set and test set. A naive Bayes classifier model is generated from the training set and used to predict the classes of the test set. The predictions are then compared to the actual classes in the test set to evaluate the accuracy of the naive Bayes model. For both datasets, the naive Bayes model is able to accurately predict most of the test instances.

1. R:
sesejun@is.ocha.ac.jp
2010/11/04

2. > options(repos=c(CRAN="http://cran.md.tsukuba.ac.jp/"))
# CRAN
> install.packages('e1071')
> library("e1071")
> contacts.train<-read.table("contacts.csv", header=T, sep=",")
> contacts.test<-read.table("contacts_test.csv", header=T, sep=",")

3. > contacts.prob<-naiveBayes(contacts.train[,-1],contacts.train[,1])
> predict(contacts.prob,contacts.test[,-1])
[1] N P
Levels: N P
> table(predict(contacts.prob,contacts.test[,-1]),contacts.test[,1])
N P
N 1 0
P 0 1
> predict(contacts.prob,contacts.train[,-1])
[1] P P P P P P N P N P
Levels: N P
> table(predict(contacts.prob,contacts.train[,-1]),contacts.train[,1])
N P
N 2 0
P 4 4

4. > iris.train<-read.table("iris_train.csv", header=T, sep=",")
> iris.test<-read.table("iris_test.csv", header=T, sep=",")
> iris.prob<-naiveBayes(iris.train[,-5],iris.train[,5])
> iris.prob
Naive Bayes Classifier for Discrete Predictors
Call:
naiveBayes.default(x = iris.train[, -5], y = iris.train[, 5])
A-priori probabilities:
iris.train[, 5]
Iris-setosa Iris-versicolor Iris-virginica
0.3583333 0.3416667 0.3000000
Conditional probabilities:
Sepal.length
iris.train[, 5] [,1] [,2]
Iris-setosa 5.000000 0.3664502
Iris-versicolor 5.960976 0.4705731
Iris-virginica 6.558333 0.6741662
...

5. > predict(iris.prob,iris.test[,-5])
[1] Iris-setosa Iris-setosa Iris-setosa
[4] Iris-setosa Iris-setosa Iris-setosa
[7] Iris-setosa Iris-setosa Iris-setosa
[10] Iris-setosa Iris-setosa Iris-setosa ...
> table(predict(iris.prob,iris.test[,-5]), iris.test[,5])
Iris-setosa Iris-versicolor Iris-virginica
Iris-setosa 43 0 0
Iris-versicolor 0 39 3
Iris-virginica 0 2 33