Bias in error estimation when using cross-validation for model selection
Abstract Background Cross-validation (CV) is an effective method for estimating TriMethylGlycine (TMG) the prediction error of a classifier.Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate.We have evaluated the validity of using the CV error estimate of