Datamining R 3rd

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Datamining R 3rd

  1. 1. R: sesejun@is.ocha.ac.jp 2009/10/28
  2. 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. 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. 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. 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

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