Homework 4
Due on 04/21/2023
1. In this exercise, we will build tree-based models using the
College
data (see “Col-
lege.csv” in Homework 2).
The response variable is the out-of-state tuition (
Outstate
).
Partition the dataset into two parts: training data (80%) and test data (20%).
(a) Build a regression tree on the training data to predict the response. Create a plot of
the tree.
(b) Perform random forest on the training data. Report the variable importance and the
test error.
(c) Perform boosting on the training data. Report the variable importance and the test
error.
2.
This problem involves the
OJ
data in the
ISLR
package.
The data contains 1070
purchases where the customers either purchased Citrus Hill or Minute Maid Orange Juice.
A number of characteristics of customers and products are recorded. Create a training set
containing a random sample of 700 observations, and a test set containing the remaining
observations.
(a) Build a classification tree using the training data, with
Purchase
as the response
and the other variables as predictors.
Which tree size corresponds to the lowest
cross-validation error? Is this the same as the tree size obtained using the 1 SE rule?
(b) Perform boosting on the training data and report the variable importance. What is
the test error rate?
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