2. The method of claim 1 , further comprising: receiving a plurality of sets of predictive modeling training data from a first plurality of client computing systems; for each set of plurality of sets of predictive modeling training data: using the training data and a plurality of training functions to train a plurality of predictive models; generating a respective score for each of the plurality of trained predictive models, where each score represents an estimation of the effectiveness of the trained predictive model; and selecting a trained predictive model from among the plurality of trained predictive models based on the generated scores; wherein a plurality of trained predictive models are thereby generated and selected; storing the plurality of selected trained predictive models in the repository of trained predictive models.
2. The method of claim 1 , further comprising: receiving a plurality of sets of predictive modeling training data from a first plurality of client computing systems; for each set of plurality of sets of predictive modeling training data: using the training data and a plurality of training functions to train a plurality of predictive models; generating a respective score for each of the plurality of trained predictive models, where each score represents an estimation of the effectiveness of the trained predictive model; and selecting a trained predictive model from among the plurality of trained predictive models based on the generated scores; wherein a plurality of trained predictive models are thereby generated and selected; storing the plurality of selected trained predictive models in the repository of trained predictive models. 3. The method of claim 2 wherein one of the training functions is maximum likelihood.