NDLI: Calibrating Random Forests

Content Provider	IEEE Xplore Digital Library
Author	Bostrom, H.
Copyright Year	2008
Description	Author affiliation: Inf. Res. Centre, Univ. of Skovde, Skovde (Bostrom, H.)
Abstract	When using the output of classifiers to calculate the expected utility of different alternatives in decision situations, the correctness of predicted class probabilities may be of crucial importance. However, even very accurate classifiers may output class probabilities of rather poor quality. One way of overcoming this problem is by means of calibration, i.e., mapping the original class probabilities to more accurate ones. Previous studies have however indicated that random forests are difficult to calibrate by standard calibration methods. In this work, a novel calibration method is introduced, which is based on a recent finding that probabilities predicted by forests of classification trees have a lower squared error compared to those predicted by forests of probability estimation trees (PETs). The novel calibration method is compared to the two standard methods, Platt scaling and isotonic regression, on 34 datasets from the UCI repository. The experiment shows that random forests of PETs calibrated by the novel method significantly outperform uncalibrated random forests of both PETs and classification trees, as well as random forests calibrated with the two standard methods, with respect to the squared error of predicted class probabilities.
Starting Page	121
Ending Page	126
File Size	159437
Page Count	6
File Format	PDF
ISBN	9780769534954
DOI	10.1109/ICMLA.2008.107
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Area measurement random forests Probability distribution Calibration Brier score Utility theory Machine learning probability-estimation trees Decision trees Positron emission tomography Informatics calibration Regression tree analysis Classification tree analysis
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
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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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