Fast Interpretable Greedy-Tree Sums (FIGS). (arXiv:2201.11931v1 [cs.LG])

Web: http://arxiv.org/abs/2201.11931

Modern machine learning has achieved impressive prediction performance, but
often sacrifices interpretability, a critical consideration in many problems.
Here, we propose Fast Interpretable Greedy-Tree Sums (FIGS), an algorithm for
fitting concise rule-based models. Specifically, FIGS generalizes the CART
algorithm to simultaneously grow a flexible number of trees in a summation. The
total number of splits across all the trees can be restricted by a
pre-specified threshold, thereby keeping both the size and number of its trees
under control. When both …

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