We examine the fluctuations in the test error induced by random, finit
e, training and test sets for the linear perceptron of input dimension
n with a spherically constrained weight vector. This variance enables
us to address such issues as the partitioning of a data set into a te
st and training set. We find that the optimal assignment of the test s
et size scales with n(2/3).