 Viewed from the perspective of the bias-variance trade-off, the ability to make accurate predictions from sparse data suggests that variance is the dominant source of error but that our cognitive system often manages to keep these errors within reasonable limits (Gigerenzer &amp; Brighton 2009). Indeed, Gigerenzer and Brighton make a stronger argument, stating that “the bias-variance dilemma shows formally why a mind can be better off with an adaptive toolbox of biased, specialized heuristics” (Gigerenzer &amp; Brighton 2009: 120); see also  section 7.2.  However, the bias-variance decomposition is a decomposition of squared loss, which means that the decomposition above depends on how total error (loss) is measured. There are many loss functions, however, depending on the type of inference one is making along with the stakes in making it. If one were to use a 0-1 loss function, for example, where all non-zero errors are treated equally—meaning that “a miss as good as a mile”—the decomposition above breaks down. In fact, for 0-1 loss, bias and variance combine multiplicatively (J. Friedman 1997)! A generalization of the bias-variance decomposition that applies to a variety of loss functions \(\mathrm{L}(\cdot)\), including 0-1 loss, has been offered by (Domingos 2000), 