Research output per year
Research output per year
Meelis Kull, Telmo M. Silva Filho, Peter Flach
Research output: Contribution to journal › Article (Academic Journal) › peer-review
For optimal decision making under variable class distributions and misclassification costs a classifier needs to produce well-calibrated estimates of the posterior probability. Isotonic calibration is a powerful non-parametric method that is however prone to overfitting on smaller datasets; hence a parametric method based on the logistic sigmoidal curve is commonly used. While logistic calibration is designed for normally distributed per-class scores, we demonstrate experimentally that many classifiers including Naive Bayes and Adaboost suffer from a particular distortion where these score distributions are heavily skewed. In such cases logistic calibration can easily yield probability estimates that are worse than the original scores. Moreover, the logistic curve family does not include the identity function, and hence logistic calibration can easily uncalibrate a perfectly calibrated classifier. In this paper we solve all these problems with a richer class of parametric calibration maps based on the beta distribution. We derive the method from first principles and show that fitting it is as easy as fitting a logistic curve. Extensive experiments show that beta calibration is superior to logistic calibration for a wide range of classifiers: Naive Bayes, Adaboost, random forest, logistic regression, support vector machine and multi-layer perceptron. If the original classifier is already calibrated, then beta calibration learns a function close to the identity. On this we build a statistical test to recognise if the model deviates from being well-calibrated.
Original language | English |
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Pages (from-to) | 5052-5080 |
Number of pages | 29 |
Journal | Electronic Journal of Statistics |
Volume | 11 |
Issue number | 2 |
DOIs | |
Publication status | Published - 15 Dec 2017 |
Research output: Contribution to journal › Article (Academic Journal) › peer-review
Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Damen, D., Gooberman-Hill, R., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.
1/10/13 → 30/09/18
Project: Research, Parent
Peter A Flach (Organiser), Miquel Perello Nieto (Organiser), Hao Song (Organiser), Meelis Kull (Organiser) & Telmo De Menezes E Silva Filho (Organiser)
Activity: Participating in or organising an event types › Participation in workshop, seminar, course
Person: Academic , Member