Lab 5

Desarrollar el laboratorio 8.3 sobre métodos basados en arboles de decisión el cual se encuentra de la pagina 324 a la 331 del libro de texto.

8.3.1 Fitting Classification Trees

library(tree)
library(ISLR)
attach(Carseats)
High=ifelse(Sales<=8,"No", "Yes")
Carseats=data.frame(Carseats,High)
str(Carseats)
tree.carseats = tree(High~.-Sales, Carseats)
summary(tree.carseats)
plot(tree.carseats)
text(tree.carseats, pretty=0)
tree.carseats

set.seed(2)
train=sample(1:nrow(Carseats), 200)
Carseats.test=Carseats[-train,]
High.test=High[-train]
tree.carseats=tree(High~.-Sales, Carseats, subset=train)
tree.pred=predict(tree.carseats, Carseats.test, type="class")
table(tree.pred, High.test)
(86+57)/200

set.seed(3)
cv.carseats=cv.tree(tree.carseats, FUN=prune.misclass)
names(cv.carseats)
cv.carseats

par(mfrow=c(1,2))
plot(cv.carseats$size, cv.carseats$dev, type ="b")
plot(cv.carseats$k, cv.carseats$dev, type="b")

prune.carseats=prune.misclass(tree.carseats, best=9)
plot(prune.carseats)
text(prune.carseats, pretty=0)

tree.pred=predict(prune.carseats, Carseats.test, type="class")
table(tree.pred, High.test)
(94+60)/200

prune.carseats=prune.misclass(tree.carseats, best=15)
plot(prune.carseats)
text(prune.carseats, pretty=0)
tree.pred=predict(prune.carseats, Carseats.test, type="class")
table(tree.pred,High.test)
(86+62)/200

8.3.2 Fitting Regressions Trees

yhat=predict(tree.boston, newdata = Boston[-train,])
boston.test=Boston[-train, "medv"]
plot(yhat, boston.test)
abline(0,1)
mean((yhat-boston.test)^2)

[1] 25.04559

8.3.3 Bagging and Random Forests

8.3.4 Boosting

boost.boston=gbm(medv~., data=Boston[train,], distribution="gaussian", n.trees=5000, interaction.depth = 4, shrinkage=0.2, verbose = F)
yhat.boost=predict(boost.boston, newdata=Boston[-train,],n.trees = 5000)
mean((yhat.boost-boston.test)^2)

[1] 11.64544