A measure of the impact of CV incompleteness on prediction error estimation with application to PCA and normalization.

ABSTRACT:

Background

In applications of supervised statistical learning in the biomedical field it is necessary to assess the prediction error of the respective prediction rules. Often, data preparation steps are performed on the dataset-in its entirety-before training/test set based prediction error estimation by cross-validation (CV)-an approach referred to as "incomplete CV". Whether incomplete CV can result in an optimistically biased error estimate depends on the data preparation step under consideration. Several empirical studies have investigated the extent of bias induced by performing preliminary supervised variable selection before CV. To our knowledge, however, the potential bias induced by other data preparation steps has not yet been examined in the literature. In this paper we

SUBMITTER: Hornung R 

PROVIDER: S-EPMC4634762 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature