1. A computer-implemented method comprising: receiving a first data set of data samples by a dynamic predictive modeling server, each data sample comprising input data and corresponding output data, wherein the first data set is new relative to a retained data set of data samples, where each data sample in the retained data set comprising input data and corresponding output data, and where the retained data set was used in training predictive models in a repository of predictive models; determining a richness score for each of the data samples included in the first data set and to each of the data samples in the retained data set, wherein the richness score for a particular data sample indicates how information rich the particular data sample is, relative to other data samples in the set of retained data samples and the first data set, for determining an accuracy of a trained predictive model, and where the richness score for a particular data sample is based, at least in part, on how redundant the particular data sample is of other, different data samples, and wherein determining, by the dynamic predictive modeling server, the richness score for each of the data samples comprises: determining a cluster to which the data sample belongs; determining first richness scores for the data sample and other data samples that belong to the cluster; determining second richness scores for the other data samples that belong to the cluster when the data sample is removed from the cluster; setting the richness score of the data sample to a null value when a sum of second richness scores for the other data samples are greater than a sum of the first richness scores for the other data samples; ranking the data samples included in the first data set and the set of retained data samples based on the assigned richness scores; and selecting a first set of test data from the data samples included in the first data set and the set of retained data samples based on the ranking.