no code implementations • 20 Feb 2024 • Brian Liu, Rahul Mazumder
We study the often overlooked phenomenon, first noted in \cite{breiman2001random}, that random forests appear to reduce bias compared to bagging.
no code implementations • 20 Feb 2024 • Brian Liu, Rahul Mazumder
We present FAST, an optimization framework for fast additive segmentation.
1 code implementation • 12 Jun 2023 • 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.
1 code implementation • 8 Jul 2022 • Brian Liu, Miaolan Xie, Haoyue Yang, Madeleine Udell
ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning.
no code implementations • 31 May 2022 • Brian Liu, Rahul Mazumder
We present ForestPrune, a novel optimization framework to post-process tree ensembles by pruning depth layers from individual trees.
1 code implementation • 1 Jul 2021 • Brian Liu, Miaolan Xie, Madeleine Udell
Like the linear LASSO, ControlBurn assigns all the feature importance of a correlated group of features to a single feature.
no code implementations • 17 Nov 2020 • Brian Liu, Madeleine Udell
Model interpretations are often used in practice to extract real world insights from machine learning models.
1 code implementation • 28 Jun 2020 • Brian Liu, Xianchao Xu, Yu Zhang
Deep learning based methods have been dominating the text recognition tasks in different and multilingual scenarios.