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Evaluating classification performance: Receiver operating characteristic and expected utility.

Yueran Yang1

  • 1Department of Psychology, University of Nevada, Reno.

Psychological Methods
|July 21, 2022
PubMed
Summary
This summary is machine-generated.

Receiver operating characteristic (ROC) analysis should incorporate prior probabilities and utilities for accurate classification performance evaluation. Expected utility (EU) lines demonstrate the link between ROC curves and expected utility, optimizing classifier selection.

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Area of Science:

  • Machine Learning
  • Statistical Analysis
  • Decision Theory

Background:

  • Receiver operating characteristic (ROC) analysis is widely used to evaluate binary classification performance.
  • Traditionally, ROC analysis is considered independent of prior probabilities and outcome utilities.
  • This independence limits its applicability in real-world decision-making scenarios.

Purpose of the Study:

  • To challenge the notion of ROC analysis independence from prior probabilities and utilities.
  • To demonstrate how to integrate prior probabilities and utilities into ROC analysis for a comprehensive performance evaluation.
  • To introduce expected utility (EU) lines as a method to connect ROC curves with expected utility.

Main Methods:

  • Development of expected utility (EU) lines to visualize the relationship between ROC curves and expected utility.
  • Application of EU lines for estimating expected utilities across various operating points and classifier types.
  • Analysis of scenarios involving single or multiple classifiers, and discrete or continuous ROC curves.

Main Results:

  • EU lines effectively bridge ROC analysis and expected utility calculations.
  • The common goal of both ROC and expected utility analysis is to maximize classification utility.
  • ROC analysis can guide the selection of optimal classifiers and operating points for maximizing expected utility.

Conclusions:

  • ROC analysis is most effective when considering prior probabilities and utilities.
  • EU lines provide a versatile tool for performance assessment in diverse classification contexts.
  • Maximizing expected utility involves more than just selecting classifiers; other approaches should also be explored.