Semisupervised methods are techniques for using labeled data (X1,Y1),…,(Xn,Yn) together with unlabeled data Xn+1,…,XN to make predictions. These methods invoke some assumptions that link the marginal distribution PX of X to the regression function f(x). For example, it is common to assume that f is very smooth over high density regions of PX. Many of the methods are ad-hoc and have been shown to work in specific examples but are lacking a theoretical foundation. We provide a minimax framework for analyzing semisupervised methods. In particular, we study methods based on metrics that are sensitive to the distribution PX. Our model includes a parameter α that controls the strength of the semisupervised assumption. We then use the data to adapt to α.