FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
This work addresses computational efficiency and interpretability issues for users of additive models in machine learning, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of slow additive segmentation in transparent machine learning by introducing FAST, an optimization framework that achieves approximately 100 times faster performance than existing state-of-the-art methods like explainable boosting machines.
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.