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Green Question



For this problem, we’ll use the mtcars data. Use the first 20 rows of mtcars to estimate a Lasso regression of mpg on cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb.

Using these estimates, predict mpg for the remaining 12 observations in mtcars. Which of these cars has the highest predicted mpg?

Rules:

To win

Raise your hand, and tell me which car has the highest predicted mpg, and your team will advance to the championship code challenge.



Solution below…















library(glmnetUtils)
train_data <- mtcars[1:20,]
test_data <- mtcars[21:(nrow(mtcars)), ]

lasso <- cv.glmnet(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am + gear + carb, data=mtcars,
                   use.model.frame=TRUE)

Yhat <- predict(lasso, newdata=test_data)

Yhat
##                  lambda.1se
## Toyota Corona      23.92724
## Dodge Challenger   17.86790
## AMC Javelin        18.06237
## Camaro Z28         16.57659
## Pontiac Firebird   16.97720
## Fiat X1-9          25.32226
## Porsche 914-2      24.70610
## Lotus Europa       26.01105
## Ford Pantera L     17.99759
## Ferrari Dino       21.10346
## Maserati Bora      16.66453
## Volvo 142E         23.13594
max(Yhat)
## [1] 26.01105

which is the predicted miles per gallon for the Lotus Europa