Abstract: A Prism Vote process can be used to make predictions based on a data set that may be heterogeneous. The data set can be stratified, e.g., using techniques adapted from principal component analysis. A prediction model can be trained independently on each stratum of the data set. To make a prediction for a “testing” data sample, the prediction model for each stratum can be used to provide a per-stratum prediction of an outcome, and a per-stratum probability that the testing data sample belongs to each stratum can be determined. The predicted outcome (e.g., a probability of a particular outcome) can be computed from the per-stratum predictions weighted according to the per-stratum probabilities.