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Regression conformal prediction with random forests
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)
University of Borås, School of Business and IT. (CSL@BS)
2014 (English)In: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 97, no 1-2, 155-176 p.Article in journal (Refereed)
Abstract [en]

Regression conformal prediction produces prediction intervals that are valid, i.e., the probability of excluding the correct target value is bounded by a predefined confidence level. The most important criterion when comparing conformal regressors is efficiency; the prediction intervals should be as tight (informative) as possible. In this study, the use of random forests as the underlying model for regression conformal prediction is investigated and compared to existing state-of-the-art techniques, which are based on neural networks and k-nearest neighbors. In addition to their robust predictive performance, random forests allow for determining the size of the prediction intervals by using out-of-bag estimates instead of requiring a separate calibration set. An extensive empirical investigation, using 33 publicly available data sets, was undertaken to compare the use of random forests to existing stateof- the-art conformal predictors. The results show that the suggested approach, on almost all confidence levels and using both standard and normalized nonconformity functions, produced significantly more efficient conformal predictors than the existing alternatives.

Place, publisher, year, edition, pages
Springer New York LLC , 2014. Vol. 97, no 1-2, 155-176 p.
Keyword [en]
Conformal prediction, Random forests, Regression, Machine learning, Data mining
National Category
Computer Science Computer and Information Science
URN: urn:nbn:se:hb:diva-2030DOI: 10.1007/s10994-014-5453-0Local ID: 2320/14623OAI: diva2:870111



work was supported by the Swedish Foundation for Strategic Research through the project High-Performance

Data Mining for Drug Effect Detection (IIS11-0053) and the Knowledge Foundation through the project Big

Data Analytics by Online Ensemble Learning (20120192).

Available from: 2015-11-13 Created: 2015-11-13

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Johansson, UlfBoström, HenrikLöfström, TuveLinusson, Henrik
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