Although PAC learning and other machine-learning methods often work well, the performance guarantees that have proven for them are typically too asymptotic to be useful. Conformal predictors, in contrast, perform equally well and come developed with simple and useful measures of confidence. Reference: Chapter 2 of Algorithmic learning in a random world, by Vladimir Vovk, Alexander Gammerman, and Glenn Shafer. Springer, to appear.