#Builder up Logistic Regression from the basics

2013, Scott Hendrickson

A short introduction to Logistic Regression. We build up the basic pieces with simple arguments and end up with a basic classifier that is simple to understand and very fast to run on some toy problems. In the end, you want to use Scikit Learn.

This session was built using:

• Python 2.7
• IPython 1.x
• numpy 1.7
• matplotlib 1.2
• pandas 0.9.1

The capability of the full sklearn package is pretty mind-blowing; this Notebook aims for the lowest hanging fruit, because the same framework is used for the advanced use-cases. This is certainly one of the strengths of sklearn. Note that these materials do not go into explaining what the various estimators are doing or how the algorithm works. For those discussions, definitely see the other materials in this repository and the official documentation.

If you want to explore the IPython Notebook without running Python on your own machine, you can also view it at nbviewer.

Enjoy!