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 Aug 2013, San Francisco, CA, USA. IEEE , 112-119 .
Abstract
Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For “black box” models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Jun 2014 10:47 |
Last Modified: | 19 Dec 2022 13:27 |
Published Version: | http://dx.doi.org/10.1109/IRI.2013.6642461 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/IRI.2013.6642461 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:79159 |