This mini-workshop explores some machine learning ideas via the Wine Quality dataset from the University of California Irvine Machine Learning Repository:

https://archive.ics.uci.edu/ml/datasets/Wine+Quality

The goal is to predict the quality of a wine, based on 11 chemical measurements available. To do this, we will use ideas from Ensemble Methods, an area of Machine Learning focused on combining weak prediction models into good ones.

The workshop is organized in 3 independent scripts, Chapter 1, 2 and 3. The scripts can be run from the F# scripting environment, without having to build anything.

Before diving into the first script, chapter-1.fsx, you will need to install 2 dependencies, fsharp.data and xplot. To do this, you can run paket, or simply run install.cmd or install.sh, depending on your operating system.

Once this is done, go to workshop/chapter-1.fsx, and start following the steps, running the code as you go. Each script contains tutorials, introducing some F# syntax, as well as sections marked [TODO], where you will have to write some code.

The folder solved/ contains a possible solution for each of the scripts, in case you get stuck.

The core ideas in this workshop are:

1. using Stumps as prediction models
2. using a Cost function to compare models
3. analyzing Residuals to improve a model, and combining "weak" Stumps into a better model
4. splitting data between training and validation to evaluate the quality of a model

Put together, these ideas give us a fairly general algorithm, which can be seen as a crude version of a more general approach, called Gradient Boosting: https://en.wikipedia.org/wiki/Gradient_boosting

Gradient Boosting takes these ideas further, by

1. generalizing this to arbitrary models, and not just stumps,
2. generalizing this to arbitrary cost functions, and not just square-of-errors.

... and putting them in the context of a general method called gradient descent.

If you are interested in seeing how this might look like, you may find this blog series interesting:

http://brandewinder.com/2016/08/06/gradient-boosting-part-1/ http://brandewinder.com/2016/08/14/gradient-boosting-part-2/ http://brandewinder.com/2016/09/03/gradient-boosting-part-3/