Packages

In this lab we will work with three packages: ISLR for the data, tidyverse which is a collection of packages for doing data analysis in a “tidy” way and tidymodels for statistial modeling.

If you’d like to run your code in the Console as well you’ll also need to load the packages there. To do so, run the following in the console.

library(tidyverse) 
library(tidymodels)
library(ISLR)

Note that the packages are also loaded with the same commands in your R Markdown document.

Data

For this lab, we are using Wage data from the ISLR package.

Exercises

1. Examine the Wage data set from the ISLR package. What are the variables? How many observations are there?

2. Create a linear model specification, setting the engine to lm. Call this model specification linear_spec.

3. Create a recipe using the Wage data from the ISLR package. We want to predict the variable wage from age, health_ins, jobclass, education, and race. We want age to be fit using a natural spline. Use tune() to decide how many degrees of freedom to use for the age variable. (In this exercise, you are just creating the recipe).

4. Use tune_grid() to fit the linear model specified in Exercise 2 with the recipe created in Exercise 3 using 10-fold cross validation, similar to Lab 04. Choose the model with the lowest RMSE. How many degrees of freedom were used for the age natural spline for this best model? Report the RMSE for this model as well as the chosen degrees of freedom.

5. Create a recipe using the Wage data from the ISLR package. We want to predict the variable wage from age, health_ins, jobclass, education, and race. We want age to be fit using a polynomial. Use tune() to decide how many degrees the polynomial should have for the age variable. (In this exercise, you are just creating the recipe).

6. Use tune_grid() to fit the linear model specified in Exercise 2 with the recipe created in Exercise 5 using 10-fold cross validation, similar to Lab 04. Choose the model with the lowest RMSE. What degree polynomial was used for age for this best model? Report the RMSE for this model as well as the chosen degree.

7. If the goal is prediction, which model would you prefer, the one fit in Exercise 4 or the one fit in Exercise 6? Using your chosen model, examine whether ridge, lasso, or elastic net would provide a better fit. Describe your results.