Explainable Boosting Machines with Sparsity -- Maintaining
  Explainability in High-Dimensional Settings
Brandon M. Greenwell Annika Dahlmann Saurabh Dhoble
Compared to "black-box" models, like random forests and deep neural networks,
explainable boosting machines (EBMs) are considered "glass-box" models that can
be competitively accurate while also maintaining a higher degree of
transparency and explainability. However, EBMs become readily less transparent
and harder to interpret in high-dimensional settings with many predictor
variables; they also become more difficult to use in production due to
increases in scoring time. We propose a simple solution based on the least
absolute shrinkage and selection operator (LASSO) that can help introduce
sparsity by reweighting the individual model terms and removing the less
relevant ones, thereby allowing these models to maintain their transparency and
relatively fast scoring times in higher-dimensional settings. In short,
post-processing a fitted EBM with many (i.e., possibly hundreds or thousands)
of terms using the LASSO can help reduce the model's complexity and drastically
improve scoring time. We illustrate the basic idea using two real-world
examples with code.