install.packages("rpart")
library(rpart)
?rpart
data(iris)
iris
av1 <- rpart(Species~.,data=iris)
av1
install.packages("DMwR")
library(DMwR)
prettyTree(av1)
?rpart.control
?rpartXse
av2 <- rpart(Species~.,data=iris,control=rpart.control(minbucket=2))
av2
prettyTree(av2)
par(mfrow=c(2,1))
prettyTree(av1)
prettyTree(av2)
dev.off()
iris
150/5
teste <- sample(150,30)
teste
av3 <- rpart(Species~.,data=iris[-teste,],control=rpart.control(minbucket=2))
prettyTree(av3)
saida <- predict(av3,newdata=iris[teste,-5])
saida
?predict
help(rpart.predict)
help(predict.rpart)
saida2 <- predict(av3,newdata=iris[teste,-5],type="c")
saida2
iris[teste,5]
tabel(saida2,iris[teste,5])
table(saida2,iris[teste,5])
install.packages("MASS")
library(MASS)
x1 <- c(rnorm(100,4,2),rnorm(60,0,4))
x2 <- c(rnorm(100,3,2),rnorm(60,-2,2))
x1
grupo c(rep("+",100),rep("-",60))
grupo <- c(rep("+",100),rep("-",60))
grupo
?plot
plot(x1,x2,col=c(rep("blue",100),rep("red",60)))
l <- lda(grupo~x1+x2)
l
z1 <- c(rnorm(10,4,2),rnorm(10,0,4))
z2 <- c(rnorm(10,3,2),rnorm(10,-2,2))
saida <- predict(l,newdata=data.frame(x1=z1, x2=z2))
saida
saida2 <- predict(l,newdata=data.frame(x1=x1, x2=x2))
saida2
table(grupo,saida2$class)
qq <- qda(grupo~x1+x2)
saida3 <- predict(qq,newdata=data.frame(x1=x1, x2=x2))
table(grupo,saida3$class)
ls()
plot(x1,x2,col=c(rep("blue",100),rep("red",60)))
install.packages("e1071")
library(e1071)
?svm
grupo
s1 <- svm(grupo~x1+x2)
s1
x<- cbind(x1,x2)
x
s1 <- svm(x,factor(grupo))
s1
plot(s1,x)
plot(s1,newdata=x)
plot(s1,x=x)
?plot.svm
plot(s1,grupo~x)
all <- cbind(x,grupo)
plot(s1,all)
plot(s1,x,factor(grupo))
library(MASS)
data(cats)
plot(cats)
cats
plot(cats[,3],cats[,2],col=ifelse(cats[,1]=="M","blue","red"))
names(cats(
))
names(cats)
s2 <- svm(Sex ~. , data=cats)
s2
plot(s2,cats)
plot(cats[,3],cats[,2],col=ifelse(cats[,1]=="M","blue","red"))
?tune
?tune.svm
?tune.svm
tt <- tune.svm(Sex~. ,data=cats, cost = 10^(-3:3), gamma = 10^(-3,1))
tt <- tune.svm(Sex~. ,data=cats, cost = 10^(-3:3), gamma = 10^(-3:1))
tt
s3 <- svm(Sex ~. , data=cats,cost=10,gamma=0.1)
plot(s3,cats)