Create DA2020_Chuah_Lab3.R

DA2020_Chuah_Lab3.R

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+library(ggplot2)
+library(rpart)
+library(rpart.plot)
+data("msleep")
+str(msleep)
+help("msleep")
+str(data)
+
+
+mSleepDF1 <- msleep[,c(3,6,10,11)]
+str(mSleepDF1)
+head(mSleepDF1)
+
+
+help("rpart")
+
+sleepModel_1 <- rpart(sleep_total ~ ., data=mSleepDF1, method = 'anova')
+sleepModel_1
+
+help("rpart.plot")
+
+rpart.plot(sleepModel_1, type = 3, fallen.leaves = TRUE)
+
+rpart.plot(sleepModel_1, type = 3, digits = 4, fallen.leaves = TRUE)
+rpart.plot(sleepModel_1, type = 3, digits = 4, fallen.leaves = TRUE)
+
+######### Iris Dataset #########
+require(C50)
+data("iris")
+head(iris)
+str(iris)
+table(iris$Species)
+
+set.seed(9850)
+grn<-runif(nrow(iris))
+
+#creating a randomized iris dataset, shuffling the dataset
+#we use order() function along with the random numbers we generated
+irisrand <- iris[order(grn),]
+
+str(irisrand)
+classificationmodel1 <- C5.0(irisrand[1:100,-5],irisrand[1:100,5])
+classificationmodel1
+summary(classificationmodel1)
+
+prediction1 <- predict(classificationmodel1,irisrand[101:150,])
+prediction1
+
+
+#confusion matrix to understand prediction data
+
+table(irisrand[101:150,5], prediction1)
+plot(classificationmodel1)
+
+
+
+library("e1071")
+classifier<-naiveBayes(iris[,1:4], iris[,5])
+table(predict(classifier, iris[,-5]), iris[,5], dnn=list('predicted','actual'))
+classifier$apriori
+classifier$tables$Petal.Length
+plot(function(x) dnorm(x, 1.462, 0.1736640), 0, 8, col="red", main="Petal length distribution for the 3 different species")
+curve(dnorm(x, 4.260, 0.4699110), add=TRUE, col="blue")
+curve(dnorm(x, 5.552, 0.5518947 ), add=TRUE, col = "green")
+
+
+
+###Rpart 1
+require(rpart)
+Swiss_rpart <- rpart(Fertility ~ Agriculture + Education + Catholic, data = swiss)
+plot(swiss_rpart) # try some different plot options
+text(swiss_rpart) # try some different text options
+
+require(party)
+
+treeSwiss<-ctree(Species ~ ., data=iris)
+plot(treeSwiss)
+
+cforest(Species ~ ., data=iris, controls=cforest_control(mtry=2, mincriterion=0))
+
+treeFert<-ctree(Fertility ~ Agriculture + Education + Catholic, data = swiss)
+
+cforest(Fertility ~ Agriculture + Education + Catholic, data = swiss, controls=cforest_control(mtry=2, mincriterion=0))
+# look at help info, vary parameters.
+
+library(tree)
+tr <- tree(Species ~ ., data=iris)
+tr
+tr$frame
+plot(tr)
+text(tr)
+#find "prettier" ways to plot the tree
+
+###Rpart 2
+
+# Conditional Inference Tree for Mileage
+fit2M <- ctree(Mileage~Price + Country + Reliability + Type, data=na.omit(cu.summary))
+summary(fit2M)
+# plot tree
+plot(fit2M, uniform=TRUE, main="CI Tree Tree for Mileage ")
+text(fit2M, use.n=TRUE, all=TRUE, cex=.8)
+
+###Rpart 3
+
+fitK <- ctree(Kyphosis ~ Age + Number + Start, data=kyphosis)
+plot(fitK, main="Conditional Inference Tree for Kyphosis")
+plot(fitK, main="Conditional Inference Tree for Kyphosis",type="simple")