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This is how you import the Carseats data into Rstudio software:
library(“ISLR”)
data(“Carseats”)
view(Carseats)
after that, please provide codes for following:
a. Split the data into a training set and a test set. b. Fit a linear model using least squares on the training set to predic
math   Statistics-And-Probability   
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Answer #1

R-code for the problem is

library(ISLR)
library(randomForest)
library(rpart)
data=Carseats
smp_size <- floor(0.5 * nrow(data))

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(data)), size = smp_size)

train <- data[train_ind, ]
test <- data[-train_ind, ]

#part b
fit <- rpart(Sales~., method="class", data=train)
printcp(fit) # display the results
plotcp(fit) # visualize cross-validation results
summary(fit) # detailed summary of splits
plot(fit, uniform=TRUE,
main="Classification Tree for Carseats")
text(fit, use.n=T, all=T, cex=.8)

#part c
fit1=randomForest(Sales~.,data=train)
print(fit1)
importance(fit1)

#part d
fit2=randomForest(Sales~.,mtry=10,data=train)

importance(fit2)

fit3=randomForest(Sales~.,mtry=2,data=train)

importance(fit3)
#here no difference in changing importance by moving mtry

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