Feature Generation with Gradient Boosted Decision Trees

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In this blog, we implement an idea previously mentioned in a Facebook AI research paper: Practical Lessons from Predicting Clicks on Ads at Facebook The idea is to create non-linear transformations of features using a gradient boosting decision tree that is then used to predict with a final estimator. The implementation is a python pipeline transformer hosted in the sktools package