Learning Data Mining with Python - Second Edition

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 Pipelinepipeline = 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 0.917, which is not as good as before, but very good for a simple binary feature model.