Beyond sigmoids: how to obtain well-calibrated probabilities from binary classifiers with beta calibration
type: Item
instance of: scholarly article
title: Beyond sigmoids: how to obtain well-calibrated probabilities from binary classifiers with beta calibration
zbMATH Open document ID: 1384.62197
DOI: 10.1214/17-EJS1338SI
author: Meelis Kull
author: Telmo M. Silva Filho
author: Peter Flach
published in: Electronic Journal of Statistics
publication date: 2018-01-12T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.ejs/1513306867
review text: A predetective model is said to be well-calibrated if its predictions match observed distributions in the data. Often used method of the calibration is a logistic calibration. A new type of calibration like calibration based on the beta distribution is considered. It has been shown that implementation of the beta calibration is easy. Several experiments are used to prove the raison d'etre of the new method.
reviewed by: Rózsa Horváth-Bokor
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62J12
zbMATH DE Number: 6825040
zbMATH Keywords: binary classification
zbMATH Keywords: classifier calibration
zbMATH Keywords: posterior probabilities
zbMATH Keywords: logistic function
zbMATH Keywords: sigmoid
zbMATH Keywords: beta distribution
Wikidata QID: Q58642598
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 6825040