FAST: An Optimization Framework for Fast Additive Segmentation in
  Transparent ML
Brian Liu Rahul Mazumder
We present FAST, an optimization framework for fast additive segmentation.
FAST segments piecewise constant shape functions for each feature in a dataset
to produce transparent additive models. The framework leverages a novel
optimization procedure to fit these models $\sim$2 orders of magnitude faster
than existing state-of-the-art methods, such as explainable boosting machines
\citep{nori2019interpretml}. We also develop new feature selection algorithms
in the FAST framework to fit parsimonious models that perform well. Through
experiments and case studies, we show that FAST improves the computational
efficiency and interpretability of additive models.