Neural Network Predictive Modeling Service

To facilitate learning, the learning rate is dynamically tuned during training. Training data are randomly partitioned into a "Train Segment" and a "Test Segment." During training on the Train Segment, the network's predictive performance is continually monitored with respect to the Test Segment to help avoid overtraining. To help find the best model and to avoid unsatisfactory local minima, a large number (almost 200) number of candidate models are built and tested for a variety of network configurations and initial weight vectors. Model selection among the candidate models is based solely on predictive performance with respect to independent test data in the Test Segment.