L02 CART

Download the repository as a zip folder and begin an R project for this lab. The zip folder will contain instructions (repeated below) and a template to get an Rmd file started.

Overview

The main goal of this lab is to continue practicing the application of tree-based methods (i.e., classification and regression trees).

Datasets

We have split the wildfires.csv dataset into a training dataset (wildfires_train.csv) and test dataset (wildfires_test.csv). They are contained in the data subdirectory along with a codebook.

Exercises

Please complete the following exercises. The document should be neatly formatted.

Exercise 1

The total area burned by a wildfire is of great concern to government planners. This is captured by the variable burned in the wildfires dataset, which is a continuous variable. In this exercise, you will train models to predict burned using other variables in the data (exclude wlf as a predictor ). Train the following candidate models:

• boosting
• bagging
• random forests
• linear regression
• ridge regression

Be sure to properly tune all of these models, including any primary tuning parameters (e.g., mtry) or tuning parameters that appear relevant. Compare the estimated test errors for each model to determine which is best.

Exercise 2

Located in the northeast of the wilderness area is a wildlife protection zone. It is home to several rare and endangered species, and thus conservationists and park rangers are very interested in whether a given wildfire is likely to reach it. In the data, fires that reach the wildlife protection zone are denoted by the indicator variable wlf. In this exercise, you will train models to predict wlf using other variables in the data (there is no exclusion on which varibles to use as predictors). Train the following candidate models:

• boosting
• bagging
• random forests
• logistic regression
• ridge logistic regression

Be sure to properly tune all of these models, including any primary tuning parameters (e.g., mtry) or tuning parameters that appear relevant. Compare the estimated test errors for each model to determine which is best.