Radial Basis Function with Deployment (Classification)

Applies Radial Basis Function (RBF) neural network architectures to classification problems; the final solution is automatically stored for deployment.

General

Element Name Description
Detail of computed results reported Detail of computed results; if Minimal detail is requested, summary statistics for the trained network and a graph of the network architecture will be displayed; at the Comprehensive level of detail, sensitivity analysis and descriptive statistics spreadsheets will be displayed; the All results level will display the predictions spreadsheets.
Missing data Specifies the substitution method for missing data.
Apply memory limit Use this option to limit the maximum data size that can be processed; note that very large data problems may require significant memory and processing resources; modify the defaults only as needed.
Memory limit Use this option to set the maximum data size that can be processed.
Save/run network file By default (Don't save trained networks), the program will simply train the network, report the results, and then discard the trained network. Use the Save network file option to save the trained network in a specific file for future application to other data; use the Run network file option to apply a previously saved network to new data.
Network file name Specifies the name of the network file to save or run; this option is not applicable if the Save/run network file option was set to Don't save trained networks.
Generate datasource, if N for input less than Generate a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.

Units

Element Name Description
Normalize variables Normalize numeric input variables and codebook vectors.
Number of hidden units Specifies the number of user-configurable hidden units.
Classification error function Specifies classification error function for output interpretation, for classification problems (with categorical output variables).

Training

Element Name Description
Radial assignment Specifies radial assignment.
Radial spread Specifies radial spread.
Radial spread value Specifies radial spread value; only applicable if Radial spread was set to Specify radial spread.
Isotropic spread value Specifies isotropic radial spread value; only applicable if Radial spread was set to Isotropic, scale by value.
K-nearest value Specifies K-nearest radial spread value; only applicable if Radial spread was set to K-nearest neighbors.

Pruning

Element Name Description
Prunes hidden units Prunes hidden units with small fan-out weights.
Pruning threshold Specifies pruning threshold.
Prunes inputs Prunes inputs with low sensitivity after training.
Sensitivity ratio Sensitivity ratio.

Predicted values

Element Name Description
Subset to generate results Specifies the subset used to generate results.

Deployment

Deployment is available if the Statistica installation is licensed for this feature.

Element Name Description
Generates C/C++ code Generates C/C++ code for deployment of predictive model.
Generates SVB code Generates Statistica Visual Basic code for deployment of predictive model.
Generates PMML code Generates PMML (Predictive Models Markup Language) code for deployment of predictive model. This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets.