Chapter 4 - Classification
Lab Solution
1 Problem 11
library(ISLR)
data("Auto")
attach(Auto)
(a). Create a binary variable, mpg01, that contains a 1 if mpg contains a value above its median, and a 0
if mpg contains a value below its median. You can compute the median using the median() function.
Note you may find it helpful to use the data.frame() function to create a single data set containing both
mpg01 and the other Auto variables.
set.seed(1)
mpg01 =rep(0,nrow(Auto))
mpg01[mpg>median(mpg)]=1
Auto =data.frame(Auto, mpg01)
(b). Explore the data graphically in order to investigate the association between mpg01 and the other fea-
tures. Which of the other features seem most likely to be useful in predicting mpg01? Scatterplots and
boxplots may be useful tools to answer this question. Describe your findings.
cor(Auto[,-9])
## mpg cylinders displacement horsepower weight acceleration
## mpg 1.0000000 -0.7776175 -0.8051269 -0.7784268 -0.8322442 0.4233285
## cylinders -0.7776175 1.0000000 0.9508233 0.8429834 0.8975273 -0.5046834
## displacement -0.8051269 0.9508233 1.0000000 0.8972570 0.9329944 -0.5438005
## horsepower -0.7784268 0.8429834 0.8972570 1.0000000 0.8645377 -0.6891955
## weight -0.8322442 0.8975273 0.9329944 0.8645377 1.0000000 -0.4168392
## acceleration 0.4233285 -0.5046834 -0.5438005 -0.6891955 -0.4168392 1.0000000
## year 0.5805410 -0.3456474 -0.3698552 -0.4163615 -0.3091199 0.2903161
## origin 0.5652088 -0.5689316 -0.6145351 -0.4551715 -0.5850054 0.2127458
## mpg01 0.8369392 -0.7591939 -0.7534766 -0.6670526 -0.7577566 0.3468215
## year origin mpg01
## mpg 0.5805410 0.5652088 0.8369392
## cylinders -0.3456474 -0.5689316 -0.7591939
## displacement -0.3698552 -0.6145351 -0.7534766
## horsepower -0.4163615 -0.4551715 -0.6670526
## weight -0.3091199 -0.5850054 -0.7577566
## acceleration 0.2903161 0.2127458 0.3468215
## year 1.0000000 0.1815277 0.4299042
## origin 0.1815277 1.0000000 0.5136984
## mpg01 0.4299042 0.5136984 1.0000000
pairs(Auto)
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