Bootstrap methods resampling techniques

Comparison of the success of different classification methods requires a realistic estimation of performance measures for classification, like misclassification rates (% wrong) or predictive abilities (% correct) for new cases (Section 5.7)—together with an estimation of the spread of these measures. Because the number of objects with known class memberships is usually small, appropriate resampling techniques like repeated double CV or bootstrap (Section 4.2) have to be applied. A difficulty is that performance measures from regression (based on residuals) are often used in the development of classifiers but not misclassification rates. 

Very often a test population of data is not available or would be prohibitively expensive to obtain. When a test population of data is not possible to obtain, internal validation must be considered. The methods of internal PM model validation include data splitting, resampling techniques (cross-validation and bootstrapping) (9,26-30), and the posterior predictive check (PPC) (31-33). Of note, the jackknife is not considered a model validation technique. The jackknife technique may only be used to correct for bias in parameter estimates, and for the computation of the uncertainty associated with parameter estimation. Cross-validation, bootstrapping, and the posterior predictive check are addressed in detail in Chapter 15. 

Jackknife (IKK), cross-validation, and the bootstrap are the methods referred to as resampling techniques. Though not strictly classified as a resampling technique, the posterior predictive check is also covered in this chapter, as it has several characteristics that are similar to resampling methods. 

To alleviate this biased estirrratiorL resampling methods, such as cross-validation and bootstrapping, can be employed to more accurately estimate prediction error. In the next sections, these techniques are described as well as the impUcations of their use in the framework of model selection and performance assessment. 

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