Implement interaction modeling in scikit-learn

It's time to add interaction features to your model. The PolynomialFeatures object in scikit-learn does just that, but here you're going to use a custom interaction object, SparseInteractions. Interaction terms are a statistical tool that lets your model express what happens if two features appear together in the same row.

SparseInteractions does the same thing as PolynomialFeatures, but it uses sparse matrices to do so. You can get the code for SparseInteractions at this GitHub Gist.

PolynomialFeatures and SparseInteractions both take the argument degree, which tells them what polynomial degree of interactions to compute.

You're going to consider interaction terms of degree=2 in your pipeline. You will insert these steps after the preprocessing steps you've built out so far, but before the classifier steps.

Pipelines with interaction terms take a while to train (since you're making n features into n-squared features!), so as long as you set it up right, we'll do the heavy lifting and tell you what your score is!

Add the interaction terms step using SparseInteractions() with degree=2. Give it a name of 'int', and make sure it is after the preprocessing step but before scaling.

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Exercise

Implement interaction modeling in scikit-learn

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