## ----IntroML-DecTree-Iris-readdata---------------------------------------
library(datasets)
str(iris)


## ----IntroML-DecTree-Iris-class1, fig.width=10, fig.height=8-------------
library(C50)
input <- iris[,1:4]
output <- iris[,5]
model1 <- C5.0(input, output, control = C5.0Control(noGlobalPruning = TRUE,minCases=1))
plot(model1, main="C5.0 Decision Tree - Unpruned, min=1")


## ----IntroML-DecTree-Iris-class1res, fig.width=10, fig.height=8----------
library(ggplot2)
ggplot(iris,aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(colour = factor(Species)))+
  # Petal.Length = 1.9 
  geom_segment(aes(x=1.9,y=min(iris$Petal.Width),xend=1.9,yend=max(iris$Petal.Width)))+ 
  # Petal.Width = 1.7
  geom_segment(aes(x=1.9,y=1.7,xend=max(iris$Petal.Length),yend=1.7))+ 
  # Petal.Length = 4.9
  geom_segment(aes(x=4.9,y=1.7,xend=4.9,yend=min(iris$Petal.Width)))+ 
  # Petal.Width = 1.5
  geom_segment(aes(x=4.9,y=1.5,xend=max(iris$Petal.Length),yend=1.5))+
  # Petal.Length = 5.4
  geom_segment(aes(x=5.4,y=1.5,xend=5.4,yend=min(iris$Petal.Width)))+ 
  ggtitle("Petal Length x Petal Width")


## ----IntroML-DecTree-Iris-class1a----------------------------------------
summary(model1)
C5imp(model1,metric='usage')


## ----IntroML-DecTree-Iris-predict----------------------------------------
newcases <- iris[c(1:3,51:53,101:103),]
newcases
predicted <- predict(model1, newcases, type="class")
predicted


## ----IntroML-DecTree-Iris-class2, fig.width=10, fig.height=8-------------
model2 <- C5.0(input, output, control = C5.0Control(noGlobalPruning = FALSE,minCases=1))
plot(model2, main="C5.0 Decision Tree - Pruned, min=1")
model2


## ----IntroML-DecTree-Iris-class3, fig.width=10, fig.height=8-------------
model3 <- C5.0(input, output, control = C5.0Control(noGlobalPruning = FALSE,minCases=5))
plot(model3, main="C5.0 Decision Tree - Pruned, min=5")