In this chapter we discuss some simple and essentially model-free methods for classification and pattern recognition. Because they are highly unstructured, they typically are not useful for understanding the nature of the relationship between the features and class outcome. However, as black box prediction engines, they can be very effective, and are often among the best performers in real data problems. The nearest-neighbor technique can also be used in regression; this was touched on in Chapter 2 and works reasonably well for low-dimensional problems. However, with high-dimensional features, the bias–variance tradeoff does not work as favorably for nearest neighbor regression as it does for classification.