Probabilistic Neural Network

Probabilistic Neural Networks (PNN) form a kernel-based estimation of the class in classification problems, using the training cases as exemplars. Every training case is copied to the hidden layer of the network, which applies a Gaussian kernel. The output layer is then reduced to a simple addition of the kernel-based estimates from each hidden unit.

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.
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.

PNN

Element Name Description
Normalize Normalize numeric input variables and codebook vectors
Includes loss matrix Includes loss matrix in probabilistic neural network.
Smoothing value A Probabilistic Neural Network (PNN) essentially constructs an estimate of the probability density function of each class by adding together Gaussian (bell-shaped) curves located at each point in the training set. The smoothing factor determines the width of these Gaussians.

Pruning

Element Name Description
Use pruning Applies sensitivity-analysis based pruning after training.
Pruning ratio Specifies classification error function for output interpretation; applicable to classification-type problems with categorical output variables, when Use pruning was selected.

Thresholds

Element Name Description
Thresholds Classification neural networks must translate the numeric level on the output neuron(s) to a nominal output variable.
Accept Specifies the accept thresholds if the option to explicitly specify thresholds is selected.
Reject Specifies the reject thresholds if the option to explicitly specify thresholds is selected.

Predicted values

Element Name Description
Subset to generate results Specifies the subset of observations to be used to compute predicted and residual values; only applicable if Comprehensive output or All results is selected as the Detail of computed results reported.

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.
Saves C/C++ code Save C/C++ code for deployment of predictive model.
File name for C/C code Specify the name and location of the file where to save the (C/C++) deployment code information.
Saves SVB code Save Statistica Visual Basic code for deployment of predictive model.
File name for SVB code Specify the name and location of the file where to save the (SVB/VB) deployment code information.
Saves PMML code Saves 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.
File name for PMML (XML) code Specify the name and location of the file where to save the (PMML/XML) deployment code information.