# 4 Classification

Categorical or qualitative outcome variables are ubiquitous. We review some supervised learning methods for classification, and see how these may be applied to observational causal inference.

Joshua Loftus
10-08-2021

## Materials

html pdf Slides Classification and logistic regression
[html] Rmd Notebook Classification, class balance, ROC curves
pdf html Rmd Notebook Gradient descent and numeric differentiation
html Exercises First exercise set

To be updated

## Preparation

• ISLR Chapter 4. This chapter is a bit lengthy, so it’s OK if you don’t carefully follow all the mathematics in sections 4.4 and 4.5 as long as you understand the concepts.
• MLstory Start Chapter 5 on optimization, read the first two sections on Optimization Basics and on Gradient Descent. You can stop when you reach Proposition 3.

## Carving nature at its joints

“A good cook gets a new knife every year; he chops! Mediocre cooks change knives monthly; they hack. My knife now has 19 years on it; it’s carved several thousand oxen and the edge is as if I had just taken it from the sharpener. Those joints have gaps, and the knife’s edge no thickness, to put something infinitesimally thin in an empty space?! Effortless! It even allows the edge wander in with ample room to play. That is why, with 19 years on it, this knife’s edge is grindstone fresh.” - Butcher Ding, the Zhuangzi

### Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

### Citation

`Loftus (2021, Oct. 8). machine learning 4 data science: 4 Classification. Retrieved from http://ml4ds.com/weeks/04-classification/`
```@misc{loftus20214,