NDLI: Interpreting random forest models using a feature contribution method

Content Provider	IEEE Xplore Digital Library
Author	Palczewska, A. Palczewski, J. Robinson, R.M. Neagu, D.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput., Univ. of Bradford, Bradford, UK (Palczewska, A.; Neagu, D.) \|\| Syngenta Ltd., Bracknell, UK (Robinson, R.M.) \|\| Sch. of Math., Univ. of Leeds, Leeds, UK (Palczewski, J.)
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.
Starting Page	112
Ending Page	119
File Size	195939
Page Count	8
File Format	PDF
ISBN	9781479910502
DOI	10.1109/IRI.2013.6642461
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vegetation Computational modeling Training Predictive models Data models Mathematical model Analytical models
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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