2014-04-04, Josh Montague
A short and basic introduction to the sklearn API interface and a couple of very simple examples of using an estimator on some built-in sample data (k-nearest neighbors and linear regression).
This notebook was built with:
- Python 3.6
- Jupyter 1.0
- matplotlib 2.0
- numpy 1.12
- sklearn 0.0
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.
The majority of this material was collected by combining pieces of the official docs (which are possibly the pinnacle of package documentation) and assorted other online materials. Instead of replicating a bunch of awesome information here, I'll suggest you read the Quick Start and as much of the tutorial as you like before getting started with this.
If you want to explore the IPython Notebook without running Python on your own machine, you can also view it at nbviewer.
Enjoy!