Optimally splitting cases for training and testing high dimensional classifiers.

ABSTRACT:

Background

We consider the problem of designing a study to develop a predictive classifier from high dimensional data. A common study design is to split the sample into a training set and an independent test set, where the former is used to develop the classifier and the latter to evaluate its performance. In this paper we address the question of what proportion of the samples should be devoted to the training set. How does this proportion impact the mean squared error (MSE) of the prediction accuracy estimate?

Results

We develop a non-parametric algorithm for determining an optimal splitting proportion that can be applied with a specific dataset and classifier algorithm. We also perform a broad simulation study for the purpose of better understanding the factors that determ

SUBMITTER: Dobbin KK 

PROVIDER: S-EPMC3090739 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature