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One Tree to Explain Them All
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2011 (English)Conference paper (Refereed)
Abstract [en]

Random forest is an often used ensemble technique, renowned for its high predictive performance. Random forests models are, however, due to their sheer complexity inherently opaque, making human interpretation and analysis impossible. This paper presents a method of approximating the random forest with just one decision tree. The approach uses oracle coaching, a recently suggested technique where a weaker but transparent model is generated using combinations of regular training data and test data initially labeled by a strong classifier, called the oracle. In this study, the random forest plays the part of the oracle, while the transparent models are decision trees generated by either the standard tree inducer J48, or by evolving genetic programs. Evaluation on 30 data sets from the UCI repository shows that oracle coaching significantly improves both accuracy and area under ROC curve, compared to using training data only. As a matter of fact, resulting single tree models are as accurate as the random forest, on the specific test instances. Most importantly, this is not achieved by inducing or evolving huge trees having perfect fidelity; a large majority of all trees are instead rather compact and clearly comprehensible. The experiments also show that the evolution outperformed J48, with regard to accuracy, but that this came at the expense of slightly larger trees.

Place, publisher, year, edition, pages
IEEE , 2011.
Keyword [en]
genetic programming, random forest, oracle coaching, decision trees, Machine learning
Keyword [sv]
Data mining
National Category
Computer Science Computer and Information Science
Research subject
Bussiness and IT
URN: urn:nbn:se:hb:diva-6680Local ID: 2320/9855ISBN: 978-1-4244-7834-7OAI: diva2:887380
IEEE Congress on Evolutionary Computation (CEC)


This work was supported by the INFUSIS project www. at the University of Skövde, Sweden, in partnership

with the Swedish Knowledge Foundation under grant


Available from: 2015-12-22 Created: 2015-12-22

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Johansson, UlfSönströd, CeciliaLöfström, Tuve
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