cm010.Rmd

---
title: "Statistical learning: classification"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(cache=TRUE)
```

# cm010 - February 8, 2017

## Overview

* Demonstrate the use of logistic regression for classification
* Identify methods for assessing classification model accuracy
* 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

* [Slides](extras/cm010_slides.html)
* [Logistic regression](stat003_logistic_regression.html)
* [Decision trees and random forests](stat004_decision_trees.html)

* [The `caret` Package](https://topepo.github.io/caret/) - 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.
* Chapters 4.1-3, 8.1, 8.2.2 in [*An Introduction to Statistical Learning*](http://link.springer.com.proxy.uchicago.edu/book/10.1007%2F978-1-4614-7138-7)

## To do for Monday

* [Start homework 6](hw06-stat-learn.html)
* Read chapter 5 in [*An Introduction to Statistical Learning*](http://link.springer.com.proxy.uchicago.edu/book/10.1007%2F978-1-4614-7138-7)