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Related Experiment Video

Updated: May 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Overfitting, generalization, and MSE in class probability estimation with high-dimensional data.

Kyung In Kim1, Richard Simon

  • 1Biometric Research Branch, National Cancer Institute, 9609 Medical Center Dr, MSC 9735 Bethesda, MD 20892-9735, USA.

Biometrical Journal. Biometrische Zeitschrift
|December 17, 2013
PubMed
Summary
This summary is machine-generated.

This study shows that some overfitting can improve class probability estimation accuracy in machine learning. Researchers found that controlled overfitting can reduce mean square error for better medical decision-making models.

Keywords:
Class probability estimationCovariance penaltyHigh-dimensional dataMean square errorOverfitting

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Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Medical Informatics

Background:

  • Accurate class probability estimation is crucial for medical decision-making.
  • Estimating probabilities is challenging with more features than cases.
  • Limited research exists on probability estimation with numerous variables.

Purpose of the Study:

  • Investigate overfitting in regularized class probability estimators.
  • Analyze the relationship between overfitting and accurate class probability estimation.
  • Clarify the link between overfitting and prediction accuracy.

Main Methods:

  • Simulation studies using real datasets.
  • Analysis of mean square error in probability estimation.
  • Development of a mean square error decomposition for class probability estimation.

Main Results:

  • Some degree of overfitting can be beneficial for reducing mean square error.
  • Overfitting impacts the accuracy of class probability estimation.
  • The proposed MSE decomposition clarifies overfitting's role in prediction accuracy.

Conclusions:

  • Controlled overfitting can enhance class probability estimation.
  • Understanding the overfitting-MSE relationship is key for accurate medical predictive models.
  • The study provides a framework for developing better probability estimators in high-dimensional settings.