Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Datasets

Marchese Robinson, RL, Palczewska, A, Palczewski, JA orcid.org/0000-0003-0235-8746 et al. (1 more author) (2017) Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Datasets. Journal of Chemical Information and Modeling, 57 (8). pp. 1773-1792. ISSN 1549-9596

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Copyright, Publisher and Additional Information: © 2017 American Chemical Society. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Information and Modeling, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jcim.6b00753. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: quantitative structure-activity relationships; model interpretation; Machine Learning; Heat Map; Random Forest; Partial Least Squares; Support Vector Machines; Support Vector Regression
Dates:
  • Published: 28 August 2017
  • Accepted: 17 July 2017
  • Published (online): 17 July 2017
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds)
The University of Leeds > Faculty of Maths and Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 19 Jul 2017 09:11
Last Modified: 17 Jul 2018 00:38
Status: Published
Publisher: American Chemical Society
Identification Number: https://doi.org/10.1021/acs.jcim.6b00753

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