cd = read.csv("http://www.rob-mcculloch.org/data/usedcars.csv")
print(dim(cd))
## [1] 20063 11
names(cd)
## [1] "price" "trim" "isOneOwner" "mileage" "year"
## [6] "color" "displacement" "fuel" "region" "soundSystem"
## [11] "wheelType"Use a single, tree, Random Forests, and boosting to relate
y=price to x=mileage.
Which works best ? Pick an out of sample experiment to justify your answer.
Plot your results.
This is just a homework, don’t kill your self trying a ton of
different tuning parameters.
Save that for the project.
Use a single, tree, Random Forests, and boosting to relate
y=price to x1=mileage and
x2=year.
Which works best ? Pick an out of sample experiment to justify your answer.
Plot your results.
This is just a homework, don’t kill your self trying a ton of
different tuning parameters.
Save that for the project.
Use a neural nets to relate y=price to
x=mileage.
Which works best ? Pick an out of sample experiment to justify your answer.
Plot your results.
This is just a homework, don’t kill your self trying a ton of
different tuning parameters.
Save that for the project.
Use a neural nets to relate y=price to
x1=mileage and x2=year.
Which works best ? Pick an out of sample experiment to justify your answer.
Plot your results.
This is just a homework, don’t kill your self trying a ton of
different tuning parameters.
Save that for the project.