1. A computer-implemented segmented predictive model method, comprising: receiving first input data into a plurality of initial predictive models to develop an initial model configuration by selecting an input data set from the plurality of predictive models using a variable selection algorithm after a training of each predictive model type is completed; receiving the input data set from said initial model configuration and a second input data as inputs into a second, model stage to develop an improvement to said initial model configuration as an output, said second input data comprising one of said first input data, data not included in said first input data, and a combination thereof; and receiving said second model stage output as an input into a third predictive model stage to develop and output a final predictive model where all input data represents a physical object or substance and where one or more weights from the final model have utility in completing a search.