8 citations · 8 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 8 cited
FIRE: An Optimization Approach for Fast Interpretable Rule Extraction
Brian Liu, Rahul Mazumder
We present FIRE, Fast Interpretable Rule Extraction, an optimization-based framework to extract a small but useful collection of decision rules from tree ensembles. FIRE selects sp…
stat.ML2022
ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles
Brian Liu, Miaolan Xie, Haoyue Yang +1
ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning. The algorithms in this packa…