Modeling Data - Data Science at the Command Line - page 150

Figure 9-7. Clustering wines with the Cobweb algorithm
Regression with SciKit-Learn Laboratory
In this section, we'll be predicting the quality of the white wine, based on their physi‐
cochemical properties. Because the quality is a number between 0 and 10, we can
consider predicting the quality as a regression task. Generally speaking, using train‐
ing data points, we train three regression models using three different algorithms.
We'll be using the SciKit-Learn Laboratory (or SKLL) package for this. If you're not
using the Data Science Toolbox, you can install SKLL using pip :
$ pip install skll
If you're running Python 2.7, you also need to install the following packages:
$ pip install configparser futures logutils
Preparing the Data
SKLL expects that the training and testing data have the same filenames, located in
separate directories. However, in this example, we're going to use cross-validation,
meaning that we only need to specify a training data set. Cross-validation is a techni‐