This module determines whether a satisfactory test set exists. If such a test set exists, the module then automatically partitions the data into training and testing data sets. ANN training requires the data to be normalized. The normalization module automatically normalizes the raw data. The user has the option of choosing between linear and nonlinear normalization of the raw data.
Normalization is otherwise transparent to the user.
Models are greatly improved because the important input variables can be identified prior to model development. The input-ranking module provides the user with a ranking of the input variables with respect to their importance for predicting the desired output. These rankings are automatically incorporated at model development time if desired. This module selects the important patterns that are required for model development. Using only the important patterns results in development of models with better generalization and also greatly reduce the time for model development.