Calibration of Probabilistic Model Output: Introduction and Online Tool

AUTHORS

Behrang Amini 1 , * , Michael Richardson 2

1 Department of Musculoskeletal Imagind, MD Anderson Cancer Center, Houston, United States

2 Department of Diagnostic Radiology, University of Washington, Seattle, United States

How to Cite: Amini B, Richardson M. Calibration of Probabilistic Model Output: Introduction and Online Tool, Iran J Radiol. 2019 ; 16(Special Issue):e99162. doi: 10.5812/iranjradiol.99162.

ARTICLE INFORMATION

Iranian Journal of Radiology: 16 (Special Issue); e99162
Published Online: December 10, 2019
Article Type: Abstract
Received: October 26, 2019
Accepted: December 10, 2019
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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.

To see Figure 1 please refer to the PDF file.

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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