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Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics.

Jacob Levman1,2,3, Bryan Ewenson1, Joe Apaloo4

  • 1Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada.

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Summary
This summary is machine-generated.

We introduce a new method to evaluate artificial intelligence (AI) models by assessing the consistency of their errors during testing. This enhanced validation technique helps create more reliable and predictable AI for various applications.

Keywords:
classificationerror consistencysupervised machine learningvalidation

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Data Science

Background:

  • Supervised machine learning classification is a prevalent form of artificial intelligence (AI) used in industry and research.
  • AI models require rigorous evaluation using hold-out validation to ensure generalizability and reliability before deployment.
  • Current validation methods do not consistently assess the patterns of errors made by AI models.

Purpose of the Study:

  • To introduce an enhanced hold-out validation technique for supervised learning classification.
  • To demonstrate how assessing the consistency of AI model mistakes improves evaluation and design.
  • To promote the development of more reliable and predictable AI technologies.

Main Methods:

  • Developed an enhanced hold-out validation technique that analyzes the consistency of sample-wise errors.
  • Applied the technique to various biomedical diagnostic AI applications.
  • Made the validation software publicly available.

Main Results:

  • The enhanced validation technique provides additional insights beyond standard methods.
  • Consistent error analysis aids in identifying weaknesses and improving AI model reliability.
  • Demonstrated the technique's utility across diverse biomedical AI applications.

Conclusions:

  • Assessing the consistency of AI model mistakes is crucial for reliable validation.
  • The enhanced technique is broadly applicable to any supervised classification task.
  • Publicly available software facilitates the creation of more dependable AI models.