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Appendix
To install it, clone the repository and follow the instructions to install the Bleeding
Edge code available at: http://scikit-learn.org/stable/install.html.
Remember to use the above repository's code, rather than the official source.
I recommend you install using virtualenv or a virtual machine, rather than
installing it directly on your computer. A great guide to virtualenv can be found
here: http://docs.python-guide.org/en/latest/dev/virtualenvs/.
More complex pipelines
http://scikit-learn.org/stable/modules/pipeline.html#featureunioncomposite-feature-spaces
The Pipelines we have used in the book follow a single stream—the output of one
step is the input of another step.
Pipelines follow the transformer and estimator interfaces as well—this allows us to
embed Pipelines within Pipelines. This is a useful construct for very complex models,
but becomes very powerful when combined with Feature Unions, as shown in the
above link.
This allows us to extract multiple types of features at a time and then combine them
to form a single dataset. For more details, see the example at: http://scikitlearn.org/stable/auto_examples/feature_stacker.html.
Comparing classifiers
There are lots of classifiers in scikit-learn that are ready to use. The one you choose
for a particular task is going to be based on a variety of factors. You can compare the
f1-score to see which method is better, and you can investigate the deviation of those
scores to see if that result is statistically significant.
An important factor is that they are trained and tested on the same data—that is,
the test set for one classifier is the test set for all classifiers. Our use of random states
allows us to ensure this is the case—an important factor for replicating experiments.
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