Techniques to improve ecological interpretability of black-box machine learning models. Case study on biological health of streams in the United States with gradient boosted trees

DOI10.1007/S13253-021-00479-7OpenAlexW3210359609MaRDI QIDQ2163504

Yanyan Li

Publication date: 10 August 2022

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-021-00479-7

zbMATH Keywords

boostinginteraction termsinterpretable machine learningmacroinvertebratesstream health

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items (1)

IADT

Uses Software

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• alr3
• gbm
• corrplot
• alr4
• ElemStatLearn
• GitHub
• iml
• SMOTE

Cites Work

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• Predictive learning via rule ensembles
• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
• Greedy function approximation: A gradient boosting machine.
• A computationally fast variable importance test for random forests for high-dimensional data
• The control of the false discovery rate in multiple testing under dependency.
• Spatially Balanced Sampling of Natural Resources
• Random forests

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