Estimating misclassification error: a closer look at cross-validation based methods.

ABSTRACT:

Background

To estimate a classifier's error in predicting future observations, bootstrap methods have been proposed as reduced-variation alternatives to traditional cross-validation (CV) methods based on sampling without replacement. Monte Carlo (MC) simulation studies aimed at estimating the true misclassification error conditional on the training set are commonly used to compare CV methods. We conducted an MC simulation study to compare a new method of bootstrap CV (BCV) to k-fold CV for estimating clasification error.

Findings

For the low-dimensional conditions simulated, the modest positive bias of k-fold CV contrasted sharply with the substantial negative bias of the new BCV method. This behavior was corroborated using a real-world dataset of prognostic gene-expression

SUBMITTER: Ounpraseuth S 

PROVIDER: S-EPMC3556102 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature