1. A machine-based method comprising in connection with a project in which a user generates a predictive model based on historical data about a system being modeled: selecting variables having at least a predetermined level of significance from a pool of potential predictor variables associated with the data, to form a population of predictor variables, extending the population to include non-linear interactions of variables, extending the population to include linear and non-linear extensions with remaining previously excluded variables, generating a possible model of the extended population of variables using a subsample of the data, determining whether the model generalizes to the data other than the subsample, if so, applying the possible model to all of the data to generate a final model, and cross-validating the final model using random portions of the data.