Training Data

Before a network can be created and trained by Qnet, data for the model must be organized and formatted for compatibility with Qnet. The files containing the training (or recall) data are specified in the training (or recall) setup dialog windows. The steps required to create training data for Qnet involve:

Gathering the training cases.

Determining what, if any, data preprocessing should take place.

Formatting the final training set for Qnet.

With backpropagation neural networks, the more training data that is gathered for the training process, the better the model will likely be. With more training cases available, the modeler is able to consider increasingly complex network designs. Gathering a large number of training cases will also make it easier to employ rigorous test sets for overtraining analysis and model integrity checks. Once the model information is gathered or generated, data preparation and formatting are required. These tasks are easily accomplished by using any of today’s popular spreadsheet or database programs. Once compiled, data may be saved to a compatible ASCII file format or directly transferred to Qnet’s DataPro for proper formatting.