1. A computer-implemented method comprising: receiving a new data set of data samples, each data sample comprising input data and corresponding output data, wherein the data set is new compared to an initial training data set and a plurality of previously received update data sets of data samples that were used to train and retrain respectively an updateable trained predictive model; assigning a richness score to each of the data samples included in the new data set and to retained data samples from the initial training data and the plurality of previously received data sets, wherein the richness score for a particular data sample indicates how information rich the particular data sample is relative to other retained data samples for determining an accuracy of the trained predictive model; ranking the data samples included in the new data set and the retained data samples based on the assigned richness scores; selecting a set of test data from the data samples included in the new data set and the retained data samples based on the ranking; and testing how accurate the trained predictive model is in determining predictive output data for given input data using the set of test data and determining an accuracy score for the trained predictive model based on the testing.