Introduction

In this lab we are going to practice fitting ensemble models. 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-06-ensemble-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 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.

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)
library(ISLR)

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

If you are working in RStudio Pro, you may need to install the package to perform boosting. You can do this by running the following once in the console:

install.packages("xgboost")

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 Carseats data from the ISLR package.

Exercises

1. Set a seed to 7. Fit a bagged decision tree estimating the carseat Sales using the remaining 10 variables. You may specify the parameters in any way that you’d like, but tune the number of trees (trees), examining 10, 25, 50, 100, 200, and 300 trees.

2. Collect the metrics from the bagged tree and filter them to only include the root mean squared error. Fill in the code below to plot these results. Describe what you see.

ggplot(---, aes(x = trees, y = mean)) + 
  geom_point() + 
  geom_line() + 
  labs(y = ---)

3. Set a seed to 7. Fit a random forest estimating the carseat Sales using the remaining 10 variables. You may specify the parameters in any way that you’d like, but tune the number of trees (trees), examining 10, 25, 50, 100, 200, and 300 trees.

4. Collect the metrics from the random forest tree and filter them to only include the root mean squared error. Using similar code as in Exercise 2, plot these results. Describe what you see.

5. Set a seed to 7. Fit a boosted tree estimating the carseat Sales using the remaining 10 variables. Specify the tree depth to be 1, the learn rate to 0.1, and tune the number of trees (trees), examining 10, 25, 50, 100, 200, and 300 trees.

6. Collect the metrics from the boosted tree and filter them to only include the root mean squared error. Using similar code as in Exercise 2, plot these results. Describe what you see.

7. Based on the exercises above and the number of trees attempted, which method would you prefer? What seems to be the optimal number of trees?

8. Extra Credit: Combine the results from the previous exercises into a single plot to describe the relationship.