Disentangled Attribution Curves for Interpreting Random Forests and
Boosted Trees

Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees

18 May 2019

W. James Murdoch

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Papers citing "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

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Title
Augmenting Interpretable Models with LLMs during Training Chandan Singh Armin Askari R. Caruana Jianfeng Gao 102 43 0 23 Sep 2022
The impact of feature importance methods on the interpretation of defect classifiers Gopi Krishnan Rajbahadur Shaowei Wang Yasutaka Kamei Ahmed E. Hassan FAtt 57 83 0 04 Feb 2022
Fast Interpretable Greedy-Tree Sums Yan Shuo Tan Chandan Singh Keyan Nasseri Abhineet Agarwal James Duncan Omer Ronen M. Epland Aaron E. Kornblith Bin Yu AI4CE 82 8 0 28 Jan 2022
Application of Machine Learning Methods in Inferring Surface Water Groundwater Exchanges using High Temporal Resolution Temperature Measurements Mohammad A. Moghaddam T. Ferré Xingyuan Chen Kewei Chen M. Ehsani AI4CE 40 4 0 03 Jan 2022
Interpreting and improving deep-learning models with reality checks Chandan Singh Wooseok Ha Bin Yu FAtt 84 3 0 16 Aug 2021
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance Mattia Carletti M. Terzi Gian Antonio Susto 50 42 0 21 Jul 2020
Transformation Importance with Applications to Cosmology Chandan Singh Wooseok Ha F. Lanusse V. Boehm Jia-Wei Liu Bin Yu AI4CE 62 11 0 04 Mar 2020
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models Benjamin J. Lengerich S. Tan C. Chang Giles Hooker R. Caruana 79 42 0 12 Nov 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 211 1,459 0 14 Jan 2019