- 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`

- 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
- Decision trees and random forests
- 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.

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