FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML

arXiv:2402.12630v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.lg dataset faster feature framework functions novel optimization orders segmentation sim stat.ml type