Most machine learning problems can either be described as target value prediction or classification problems. There are a number of ways in which classification problems can be reduced to target value prediction problems. In PADO this is done by evolving ``discriminator'' programs for each of the classes and then orchestrating their responses. This means that for each program in PADO, the value it should return is ordinal (i.e. if v is the right value to return, then a program that says v-1 is better than a program that says v-2). Having collapsed these two parts of machine learning into one view, let us consider an abstract input to output mapping to be learned by the neural programs.