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Related Experiment Video

Updated: Jun 2, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Optimally splitting cases for training and testing high dimensional classifiers.

Kevin K Dobbin¹, Richard M Simon

¹Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA. dobbinke@uga.edu

BMC Medical Genomics

|April 12, 2011

Summary

This summary is machine-generated.

Determining the optimal training/testing split proportion is crucial for accurate predictive classifiers from high-dimensional data. Our study introduces a non-parametric method to find this optimal proportion, minimizing prediction error.

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Area of Science:

High-dimensional data analysis
Machine learning and statistical modeling

Background:

Developing predictive classifiers from high-dimensional data often involves splitting samples into training and testing sets.
The proportion of samples allocated to the training set significantly impacts the accuracy estimate of the classifier's performance.

Purpose of the Study:

To investigate how the training set proportion affects the mean squared error (MSE) of prediction accuracy estimates.
To develop an optimal sample splitting strategy for predictive classifier development.

Main Methods:

Developed a non-parametric algorithm to determine the optimal sample splitting proportion for a given dataset and classifier.
Conducted extensive simulations to understand factors influencing optimal splits and evaluate common strategies (e.g., 1/2 or 2/3 training).
Decomposed MSE into three component parts to analyze prediction accuracy.

Main Results:

The optimal training set proportion for linear classifiers depends on the total sample size and the degree of differential expression.
Higher classification accuracy and smaller total sample sizes (n) favor allocating a larger proportion to the training set.
The common 2/3 training strategy is near-optimal for datasets with n ≥ 100 and strong signals (≥85% accuracy).

Conclusions:

Recommended a non-parametric resampling approach for determining the optimal data split, applicable to any dataset and predictor development method.
The optimal split proportion is dataset- and classifier-specific, influenced by sample size and signal strength.
Validated findings on synthetic and real microarray datasets.