To see Figure 1 please refer to the PDF file.
An Innovative Journal in the Field of Radiology
e-ISSN 2008-2711
p-ISSN 1735-1065
Calibration of Probabilistic Model Output: Introduction and Online Tool
Authors
1Department of Musculoskeletal Imagind, MD Anderson Cancer Center, Houston, United States
2Department of Diagnostic Radiology, University of Washington, Seattle, United States
*Corresponding Author: Department of Musculoskeletal Imagind, MD Anderson Cancer Center, Houston, United States. Email: bamini@mdanderson.org
Published online:Dec 10, 2019
Article type:Abstract
Received:Oct 26, 2019
Accepted:Dec 10, 2019
How to Cite:Amini B, Richardson M. Calibration of Probabilistic Model Output: Introduction and Online Tool. I J Radiol. 2019;16(Special Issue):e99162. doi: https://doi.org/10.5812/iranjradiol.99162
Abstract
Many machine learning algorithms provide probabilistic predictions as their outputs. Analysis techniques familiar to physicians (e.g., calculation of sensitivity and specificity and construction of receiver operating characteristics curves) do not allow for the assessment of model calibration and prevent proper evaluation of these models. We reviewed statistical and graphical (shown in Figure 1) methods for calibration analysis and presented a framework for the implementation of these techniques using open-source codes and an online tool.
Highlights
Copyright
Copyright © 2019, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
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