Measuring regional effects of model inputs with random Forest

DOI10.1080/03610918.2018.1520871OpenAlexW2901117222WikidataQ128986054 ScholiaQ128986054MaRDI QIDQ5088125

Pengfei Wei, Jingwen Song, Zhen-Zhou Lü

Publication date: 4 July 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2018.1520871

zbMATH Keywords

high-dimensional modelrandom forestregional analysispermutation variable importance measurevariance-based indices

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

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Cites Work

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• Correlation and variable importance in random forests
• Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
• Empirical characterization of random forest variable importance measures
• Statistical modeling: The two cultures. (With comments and a rejoinder).
• Analysis of variance designs for model output
• A probabilistic procedure for quantifying the relative importance of model inputs characterized by second-order probability models
• Borgonovo moment independent global sensitivity analysis by Gaussian radial basis function meta-model
• Derivative based global sensitivity measures and their link with global sensitivity indices
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
• Random forests

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