Given that a set of features has been selected, one should be able to design a classifier that takes as input microarray data for an unknown sample and generates as output a predicted class label (e.g., clinical outcome, kind of infection, or other conditions). Figure 5 illustrates this approach in a hypothetical functional immunomics application. In this case, there are two class labels, corresponding to control and protected patients, in a situation in which protection is achieved by immunization with an attenuated-virus vaccine for a given infectious disease. The objective is to identify the epitopes that show a discriminatory response between the two groups and are therefore prime targets for rational epitope-based vaccine design. A set of two features, corresponding to epitopes X and Y, have been identified via feature selection among the thousands of microarray probes. For instance, we can imagine a situation in which the protected individuals presented a higher TNF-a response to epitope X, as well as a higher IFN-c response to epitope Y. Based on the response values observed for each patient (note that a patient corresponds to a point in the plane), a linear classifier is designed. This classifier corresponds simply to two decision regions separated by a line. If a future unknown patient has response values that fall in the upper decision region, he/she is likely to be a protected patient, provided that the classifier has a small probability of error. In this case, the responses to epitopes X and Y (in terms of the TNF-a and IFN-c cytokines) characterize immunological memory induced by the attenuated-virus vaccine: large response values to both epitopes X and Y indicate protected patients (note that the response of neither epitope X nor epitope Y by itself is a good discriminator in this example, indicating the need to consider the multivariate, combined effect of both responses). Note, in Figure 5 , that the apparent error rate (i.e., the number of misclassified sample divided by the total number of samples) is 2 4 20 ¼ 10%; the actual probability of classification error on future data typically exceeds the apparent error rate [106] .