Learning Data Mining with Python

Classifying with scikit-learn Estimators
Putting it all together
We can now create a workflow by combining the code from the previous sections,
using the broken dataset previously calculated:
X_transformed = MinMaxScaler().fit_transform(X_broken)
estimator = KNeighborsClassifier()
transformed_scores = cross_val_score(estimator, X_transformed, y,
scoring='accuracy')
print("The average accuracy for is
{0:.1f}%".format(np.mean(transformed_scores) * 100))
This gives us back our score of 82.3 percent accuracy. The MinMaxScaler resulted in
features of the same scale, meaning that no features overpowered others by simply
being bigger values. While the Nearest Neighbor algorithm can be confused with
larger features, some algorithms handle scale differences better. In contrast, some
are much worse!
Pipelines
As experiments grow, so does the complexity of the operations. We may split up
our dataset, binarize features, perform feature-based scaling, perform sample-based
scaling, and many more operations.
Keeping track of all of these operations can get quite confusing and can result in
being unable to replicate the result. Problems include forgetting a step, incorrectly
applying a transformation, or adding a transformation that wasn't needed.
Another issue is the order of the code. In the previous section, we created our
X_transformed dataset and then created a new estimator for the cross validation.
If we had multiple steps, we would need to track all of these changes to the dataset
in the code.
Pipelines are a construct that addresses these problems (and others, which we will
see in the next chapter). Pipelines store the steps in your data mining workflow. They
can take your raw data in, perform all the necessary transformations, and then create
a prediction. This allows us to use pipelines in functions such as cross_val_score,
where they expect an estimator. First, import the Pipeline object:
from sklearn.pipeline import Pipeline
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