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Venn predictors for well-calibrated probability estimation trees
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).ORCID iD: 0000-0003-0412-6199
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).ORCID iD: 0000-0003-0274-9026
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
Department of Information Technology, University of Borås, Sweden.
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2018 (English)In: Conformal and Probabilistic Prediction and Applications / [ed] A. Gammerman, V. Vovk, Z. Luo, E. Smirnov, & R. Peeters, 2018, p. 3-14Conference paper, Published paper (Refereed)
Abstract [en]

Successful use of probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. The standard solution is to employ an additional step, transforming the outputs from a classifier into probability estimates. In this paper, Venn predictors are compared to Platt scaling and isotonic regression, for the purpose of producing well-calibrated probabilistic predictions from decision trees. The empirical investigation, using 22 publicly available data sets, showed that the probability estimates from the Venn predictor were extremely well-calibrated. In fact, in a direct comparison using the accepted reliability metric, the Venn predictor estimates were the most exact on every data set.

Place, publisher, year, edition, pages
2018. p. 3-14
Series
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 91
Keywords [en]
Venn predictors, Calibration, Decision trees, Reliability
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-43505OAI: oai:DiVA.org:hj-43505DiVA, id: diva2:1306288
Conference
Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, 11-13 June 2018
Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-08-22Bibliographically approved

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