In the lab, a classification tree was applied to the Carseats
data set after converting Sales
into a qualitative response variable.
Now we will seek to predict Sales
using regression trees and related approaches, treating the response as a quantitative variable.
We first split the data in test and training set.
We already provide you with this code in order to align our starting data set.
library(ISLR2)
set.seed(1)
train <- sample(1:nrow(Carseats), nrow(Carseats) / 2)
Carseats.train <- Carseats[train, ]
Carseats.test <- Carseats[-train, ]
Fit a regression tree to the training set.
Use a seed value of 2.
Plot the tree, and interpret the results.
Store the test error in mse.regrtree
.
Use the bagging approach in order to analyze this data.
Use 500 trees and a seed value of 3.
Store the test error in mse.bag
.
Use the importance()
function to determine which variables are most important.
Use random forests to analyze the data.
Use a seed value of 4.
Use 500 trees and use the typical \(m = \sqrt{p}\) (rounded to the nearest integer).
Store the test error rate in mse.rf
.
Use the importance()
function to determine which variables are most important.
Assume that:
ISLR2
, tree
and randomForest
libraries have been loaded