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pro vyhledávání: '"McTavish, Hayden"'
Many important datasets contain samples that are missing one or more feature values. Maintaining the interpretability of machine learning models in the presence of such missing data is challenging. Singly or multiply imputing missing values complicat
Externí odkaz:
http://arxiv.org/abs/2412.02646
Autor:
McTavish, Hayden, Zhong, Chudi, Achermann, Reto, Karimalis, Ilias, Chen, Jacques, Rudin, Cynthia, Seltzer, Margo
Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady
Externí odkaz:
http://arxiv.org/abs/2112.00798
Autor:
McTavish, Hayden, Zhong, Chudi, Achermann, Reto, Karimalis, Ilias, Chen, Jacques, Rudin, Cynthia, Seltzer, Margo
Publikováno v:
Proc Conf AAAI Artif Intell
Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7feeae1c3282e1f75270a4f5d9ef528
https://pubmed.ncbi.nlm.nih.gov/36051654
https://pubmed.ncbi.nlm.nih.gov/36051654
