 Now imagine our prediction of Y is based on some data \(\mathcal{D}\) about the relationship between X and Y, such as last year’s daily temperatures and daily total sales of gelato in Rome. The role that this particular dataset \(\mathcal{D}\) plays as opposed to some other possible data set is a detail that will figure later. For now, view our prediction of Y as some function of X, written \(h(X)\). Here again we wish to pick an \(h(\cdot)\) to minimize \(\mathbb{E} \left[ (Y - h(X))^2 \right]\), but how close \(h(\cdot)\) is to Y will depend on the possible values of X, which we can represent by the conditional expectation 