This content is from the fall 2016 version of this course. Please go here for the most recent version.

cm016 - November 16, 2016

Overview

Define resampling methods
Review the validation set approach using linear regression
Explain leave-one-out cross-validation
Explain \(k\)-fold cross-validation
Demonstrate how to conduct cross-validation on generalized linear models
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

Slides and links

Notes from class
Slides
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.

To do for Monday

Start homework 8
Final projects people! Get started on them.
- Starter repo for the project - check this demo out of an R Markdown website. This is what you will start with in your final project repo.
- Rendered version of the template
- Clone your final project repo

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