Overview

• Define a decision tree
• Demonstrate how to estimate a decision tree
• Define and estimate a random forest
• Introduce the caret package for statistical learning in R
• Define resampling method
• Compare and contrast the validation set approach with leave-one-out and k-fold cross-validation
• Demonstrate how to conduct cross-validation using rsample

Before class

This is not a math/stats class. In class we will briefly summarize how these methods work and spend the bulk of our time on estimating and interpreting these models. That said, you should have some understanding of the mathematical underpinnings of statistical learning methods prior to implementing them yourselves. See below for some recommended readings:

• Chapters 8.1, 8.2.2, and 5.1 in An Introduction to Statistical Learning

Class materials

• Decision trees and random forests

• Cross-validation methods

• The caret Package - introductory book for the caret package. Tells you what models you can implement and all the nitty-gritty details to customize train for different cross-validation methods.

• Working with rsets - documentation for rsample and demonstration implementing it for resampling and model assessment

What you need to do after class

• Complete the statistical learning homework