Palczewska, AM, Palczewski, J, Marchese Robinson, R et al. (1 more author) (2014) Interpreting random forest classification models using a feature contribution method. In: Bouabana-Tebibel, T and Rubin, SH, (eds.) Integration of Reusable Systems. Springer , 193 - 218 (26). ISBN 3319047167
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 relatively 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. By analysing feature contributions for a training dataset, the most significant variables can be determined and their typical contribution towards predictions made for individual classes, i.e., class-specific feature contribution “patterns”, are discovered. These patterns represent a standard behaviour of the model and allow for an additional assessment of the model reliability for new data. 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: | Book Section |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2014, Springer. This is an author produced version of a paper published in Integration of Reusable Systems. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-04717-1_9 |
Keywords: | Computers |
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: | 10 Jun 2014 08:50 |
Last Modified: | 26 Apr 2015 12:19 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-04717-1_9 |
Status: | Published |
Publisher: | Springer |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-04717-1_9 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:79160 |