cm012 - May 2, 2018

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 modelr

Before class

Read chapters , 8.1, 8.2.2, and 5.1 in An Introduction to Statistical Learning if you want a rigorous introduction to the mathematics behind logistic regression, decision trees, and random forests. In class we will briefly summarize how these methods work and spend the bulk of our time on estimating and interpreting these models

Slides and links

Slides
Decision trees and random forests
Resampling 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.
k-fold cross validation with modelr and broom

What you need to do

Start homework 6

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