Publication

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

Mauro Delorenzi, Vlad Popovici
2010
Journal paper

Abstract

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints.

Official source

https://infoscience.epfl.ch/record/172715?ln=en

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Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

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