Learning Data Mining with Python

Chapter 5
Putting it all together
Now that we have a tested transformer, it is time to put it into action. Using what
we have learned so far, we create a Pipeline, set the first step to the MeanDiscrete
transformer, and the second step to a Decision Tree Classifier. We then run a cross
validation and print out the result. Let's look at the code:
from sklearn.pipeline import Pipeline
pipeline = Pipeline([('mean_discrete', MeanDiscrete()),
('classifier', DecisionTreeClassifier(random_state=14))])
scores_mean_discrete = cross_val_score(pipeline, X, y,
scoring='accuracy')
print("Mean Discrete performance:
{0:.3f}".format(scores_mean_discrete.mean()))
The result is 0.803, which is not as good as before, but not bad for simple
binary features.
Summary
In this chapter, we looked at features and transformers and how they can be
used in the data mining pipeline. We discussed what makes a good feature
and how to algorithmically choose good features from a standard set. However,
creating good features is more art than science and often requires domain
knowledge and experience.
We then created our own transformer using an interface that allows us to use it
in scikit-learn's helper functions. We will be creating more transformers in later
chapters so that we can perform effective testing using existing functions.
In the next chapter, we use feature extraction on a corpus of text documents.
There are many transformers and feature types for text, each with their advantages
and disadvantages.
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