Introduction

In this lab we are going to practice cross validation. A few reminders:

• Remember to label your chunks
• Write out your answers in full sentences, do not just rely on the R output
• Knit, commit, and push regularly (at least after every exercise)

Getting started

• Go to our class’s GitHub organization sta-363-s20
• Find the GitHub repository (which we’ll refer to as “repo” going forward) for this lab, lab-04-ridge-lasso-elastic-YOUR-GITHUB-HANDLE. This repo contains a template you can build on to complete your assignment.
• On GitHub, click on the green Clone or download button, select Use HTTPS. Click on the clipboard icon to copy the repo URL.
• If using RStudio Cloud, go to RStudio Cloud and into the course workspace. Create a New Project from Git Repo. You will need to click on the down arrow next to the New Project button to see this option.
• If using RStudio Pro, create a new project by clicking File > New Project Then click Version Control and Git/Github.
• Copy and paste the URL of your assignment repo into the dialog box.
• Hit OK, and you’re good to go!

Packages

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

Now that the necessary packages are installed, you should be able to Knit your document and see the results.

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)

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

Housekeeping

git config --global user.email "your email"
git config --global user.name "your name"

git config --global user.email
git config --global user.name

git config --global credential.helper 'cache --timeout 604800'

Warm up

Before we introduce the data, let’s warm up with some simple exercises.

Data

For this lab, we are using a data frame currently in music.csv. This data frame includes 72 predictors that are components of audio files and one outcome, lat, the latitude of where the music originated. We are trying to predict the location of the music’s origin using audio components of the music.

Exercises

1. Set a seed of 7. Split the music data into a training and test set with 50% of the data in the training and 50% in the testing. Call your training set music_train and your testing set music_test. Describe these data sets (how many observations, how many variables).

music <- read_csv("music.csv")

## Parsed with column specification:
## cols(
##   .default = col_double()
## )

## See spec(...) for full column specifications.

2. We are interested in predicting the latitude (lat) of the music’s origin from all other variables. Fit a linear model using least squares on the training set. Report the test root mean squared error obtained.

3. Fit a ridge regression model on the training set with \(\lambda\) chosen using 10-fold cross valiation. Report the \(\lambda\) chosen and explain why. Report the test root mean squared error obtained using testing portion of the initially split data frame.

4. Fit a lasso model on the training set with \(\lambda\) chosen using cross valiation. Report the \(\lambda\) chosen and explain why. Report the test root mean squared error obtained using testing portion of the initially split data frame.

5. Fit an elastic net model on the training set with \(\lambda\) and \(\alpha\) chosen using cross valiation. Report the \(\lambda\) chosen and explain why. Report the test root mean squared error obtained using testing portion of the initially split data frame.

6. Comment on the results obtained. How accurately can we predict the latitude of where the music originated? Is there much difference among the test errors resulting from these four approaches?