library(ISLR)
set.seed(1)
train=sample(392,196)
lm.fit=lm(mpg~horsepower,data=Auto,subset=train)
attach(Auto)
mean((mpg-predict(lm.fit,Auto))[-train]^2)
lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train)
mean((mpg-predict(lm.fit2,Auto))[-train]^2)
lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train)
mean((mpg-predict(lm.fit3,Auto))[-train]^2)
set.seed(2)
train=sample(392,196)
lm.fit=lm(mpg~horsepower,subset=train)
mean((mpg-predict(lm.fit,Auto))[-train]^2)
glm.fit=glm(mpg~horsepower,data=Auto)
coef(glm.fit)
lm.fit=lm(mpg~horsepower,data=Auto)
coef(lm.fit)

library(boot)
glm.fit=glm(mpg~horsepower,data=Auto)
cv.err=cv.glm(Auto,glm.fit)
cv.err$delta
cv.err=rep(0,5)
library(boot)  # Ensure the boot library is loaded

cv.err <- rep(0, 5)  # Initialize a vector to store CV errors for degrees 1 to 5

for (i in 1:5) {
  glm.fit <- glm(mpg ~ poly(horsepower, i), data = Auto)  # Fit polynomial model
  cv.err[i] <- cv.glm(Auto, glm.fit)$delta[1]  # Store the CV error for degree i
}

print(cv.err)  # Display the cross-validation errors