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Updated: May 23, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Prospect certainty for data-driven models.

Qais Yousef1, Pu Li2

  • 1Group of Process Optimization, Institute for Automation and Systems Engineering, Technische Universität Ilmenau, P.O. Box 100565, 98684, Ilmenau, Germany. qais.yousef@tu-ilmenau.de.

Scientific Reports
|March 11, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel method to quantify output certainty in data-driven models, addressing input uncertainty. The approach enhances model robustness by evaluating output confidence, improving reliability in real-world applications.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Data-driven models face challenges with output uncertainty due to inherent input variability.
  • Lack of reference data during deployment hinders the ascertainment and acceptance of model outputs.
  • Evaluating output certainty is crucial for enhancing the robustness of data-driven models.

Purpose of the Study:

  • To present a novel method for quantifying the output certainty of data-driven models.
  • To address the impact of changing input data probability distributions on model outputs.
  • To improve the reliability and practical applicability of data-driven models.

Main Methods:

  • Introduced logit masking to mitigate model determinism and generate output logit alternatives.
Keywords:
Data-driven modelDistributional changeProspect theoryUncertainty

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  • Proposed a weighted probability function to assess the certainty of generated alternatives.
  • Defined a behavior function to analyze the impact of alternatives on output distribution patterns.

    • Main Results:

    • The proposed method effectively quantifies output certainty for data-driven models.
    • Experimental results demonstrate superior performance compared to state-of-the-art techniques.
    • The method successfully refines model outputs by selecting the most certain variant.

    Conclusions:

    • The developed method enhances the robustness and trustworthiness of data-driven models.
    • Quantifying output certainty is vital for deploying models in uncertain environments.
    • This approach offers a significant advancement in evaluating and improving model reliability.