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Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability.

Sung Yang Ho1, Kimberly Phua1, Limsoon Wong2

  • 1School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.

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Summary
This summary is machine-generated.

Internal validation methods for machine learning models may be unreliable. External validation using independent datasets offers a more robust approach to assess model generalizability and data quality.

Keywords:
computational biologydata sciencedescriptive statisticsexploratory data analysisscientific method

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Area of Science:

  • Machine Learning
  • Data Science
  • Statistical Modeling

Background:

  • Model validation is crucial for assessing machine learning (ML) model efficacy and generalizability.
  • Current internal validation techniques (e.g., cross-validation, bootstrap) have limitations.
  • These limitations stem from potential training data bias and procedural complexities, hindering reliable quality assessment.

Purpose of the Study:

  • To highlight the limitations of internal validation for ML models.
  • To advocate for the adoption of external validation using independent datasets.
  • To propose novel extensions and procedures for robust external validation.

Main Methods:

  • Critically analyze the shortcomings of internal validation methods.
  • Introduce and elaborate on the concept and necessity of external validation.
  • Propose two extensions to the external validation approach.
  • Suggest a procedure for validating external datasets and statistical reference points for data quality assessment.

Main Results:

  • Internal validation methods are insufficient for guaranteeing ML model quality.
  • External validation provides a more reliable assessment of model generalization.
  • Proposed extensions enhance the ability to identify domain-relevant models.
  • A framework for validating external datasets and detecting data issues is presented.

Conclusions:

  • External validation is essential for robust ML model evaluation.
  • The proposed extensions and validation procedures improve the reliability of external validation.
  • Addressing data quality in external validation is critical for trustworthy ML applications.