Chapter 3 provides an overview of all elements of the predictive modeling process, from the selection of training and test data sets, parallel multivariate feature selection experiments and deciding on an optimal multivariate biomarker, to building, tuning, validating, and testing predictive models implementing the optimal biomarker. Discussed are also such topics as bias-variance tradeoff, segmentation models, and committees of predictive models.