Interpreting random forest models using a feature contribution method

Palczewska, A, Palczewski, J, Robinson, RM et al. (1 more author) (2013) Interpreting random forest models using a feature contribution method. In: Information Reuse and Integration (IRI), 2013 IEEE 14th International Conference on. 2013 IEEE 14th International Conference on Information Reuse and Integration, 14 - 16 August 2013, San Francisco, CA, USA. IEEE , 112-119 .

Abstract

Metadata

Authors/Creators:
  • Palczewska, A
  • Palczewski, J
  • Robinson, RM
  • Neagu, D
Copyright, Publisher and Additional Information: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Keywords: Random processes; regression analysis; UCI benchmark datasets; black box models; feature contribution method; feature contributions; linear regressions; model evaluation process; model interpretation; model parameters; model prediction; model structure; random forest classification models; statistical models; analytical models; computational modeling; data models; mathematical model; predictive models; training; vegetation
Dates:
  • Published: 2013
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Maths and Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 06 Jun 2014 10:47
Last Modified: 15 Jan 2018 18:42
Published Version: http://dx.doi.org/10.1109/IRI.2013.6642461
Status: Published
Publisher: IEEE
Refereed: Yes
Identification Number: https://doi.org/10.1109/IRI.2013.6642461

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