Rather than coming up with term and document weighting functions by hand, as we primarily did in Chapter 6 , we can view different sources of relevance signal (cosine score, title match, etc.) as features in a learning problem. A classifier that has been fed examples of relevant and nonrelevant documents for each of a set of queries can then figure out the relative weights of these signals. If we configure the problem so that there are pairs of a document and a query which are assigned a relevance judgment of relevant or nonrelevant, then we can think of this problem too as a text classification problem. Taking such a classification approach is not necessarily best, and we present an alternative in Section 15.4.2 . Nevertheless, given the material we have covered, the simplest place to start is to approach this problem as a classification problem, by ordering the documents according to the confidence of a two-class classifier in its relevance decision. And this move is not purely pedagogical; exactly this approach is sometimes used in practice.