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Model validation software for classification models using repeated partitioning: MVREP.

Wei Li1, Vincent C Arena, Nancy B Sussman

  • 1Department of Biostatistics, University of Pittsburgh, 318 Parran Hall, 130 De Soto Street, Pittsburgh, PA 15261, USA.

Computer Methods and Programs in Biomedicine
|July 10, 2003
PubMed
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This study introduces Model Validation using Repeated Partitioning (MVREP) software for computational model assessment. MVREP provides robust estimates and variability assessments for prediction measures, enhancing model validation accuracy.

Area of Science:

  • Computational toxicology
  • Biostatistics
  • Cheminformatics

Background:

  • Model validation is crucial for assessing computational model prediction ability.
  • Prediction measures like sensitivity, specificity, and accuracy quantify performance for categorical outcomes.
  • Existing methods may not fully capture the variability in prediction measures.

Purpose of the Study:

  • To introduce Model Validation using Repeated Partitioning (MVREP) software.
  • To implement a computer-intensive, nonparametric re-partitioning method for model validation.
  • To generate empirical sampling distributions of prediction measures for robust assessment.

Main Methods:

  • Developed MVREP using SAS Macro language.
  • Employed a re-partitioning method involving repeated random data partitioning.

Related Experiment Videos

  • Integrated standard validation procedures like cross-validation within the repeated partitioning process.

    • Main Results:

    • MVREP generates empirical sampling distributions for prediction measures.
    • The means of these distributions provide point estimates for model performance.
    • The variances offer direct assessment of the variability in these estimates.

    Conclusions:

    • MVREP offers a robust approach to model validation for computational models.
    • The software facilitates accurate assessment of prediction measures and their variability.
    • Demonstrated utility in evaluating structure-activity relationships models using a toxicity dataset.