timbook / ga-glm-lecture

Cheat sheets, data, and code associated with my guest lecture at General Assembly on July 21st, 2017.

GA Guest Lecture on GLMs

The gracious people at General Assembly allowed me to give a guest lecture on a topic I am quite partial to: generalized linear models (GLMs).

Data

Much of the data used here come from UCLA's IDRE tutorial modules. I claim no rights to owning them. I make no profit from using this data. The rest of the data is open source. I used the famous iris data, as well as the ggplot2-included diamonds data.

Notebooks

The lecture features intermittent code-a-longs. The python-notebooks directory contains Jupyter notebooks for the accompanying Python code. Feel free to clone this repository down and try the code out yourself!

R Code

I believe it is important to be language-agnostic. Since R is my native tongue, I have also replicated all of the Python code in the python-notebooks folder into R for reference. This can be found in the R-notebooks folder. The 03 file in the R-notebooks folder contains an example of cumulative logistic regression - something not easily implemented in Python.

Presentation

I am still considering whether or not I want to upload the full presentation. I am leaning towards no, but feel free to contact me and ask anyway!

However, here is a list of topics I cover in the presentation:

• Recap OLS
• Recap logistic regression
• Discuss the general form of a GLM
• Discuss some common GLMs and their applications
• A brief interlude on model inference
• GLMs for classification
  • Logistic regression for classification
  • Multinomial regression for classification
  • Cumulative logit regression for classification
• GLM Pot Pourri