Applies Multilayer Perceptron (MLP) neural network architectures to regression or classification problems, or mixtures of regression and classification problems. MLP neural network architectures with 1, 2, or 3 layers can be specified, for problems with continuous and/or categorical predictors. Note: Multilayer perceptrons with many variables and 2 or 3 layers can require a substantial amount of training time, even on fast computers or networks. Always start with simple architectures with few variables and 1 or 2 layers, before applying more complex models.
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 and residuals spreadsheets (when applicable).
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.
Units
Element Name
Description
Number of hidden layers
Specifies the number of hidden layers for the multilayer perceptron, and the number of units in each layer. Note: Multilayer perceptrons with many variables and 2 or 3 layers can require a substantial amount of training time, even on fast computers or networks. Always start with simple architectures with few variables and 1 or 2 layers, before applying more complex models.
Number of units layer 1
Specifies the number of units for hidden layer 1.
Number of units layer 2
Specifies the number of units for hidden layer 2; not applicable if the Number of hidden layers was selected to be 1.
Number of units layer 3
Specifies the number of units for hidden layer 3; not applicable if the Number of hidden layers was selected to be 2 or less.
Output functions
Element Name
Description
Classification error function
Specifies classification error function for output interpretation; applicable to classification-type problems with categorical output variables.
Regression output function
Specifies regression output function for output interpretation; applicable to regression-type problems with continuous outputs.
Logistic-range parameter
Multilayer perceptron regression output function logistic-range; applicable on if Logistic-range Regression output function was selected for regression-type problems with continuous outputs.
Training
Element Name
Description
Training phase one
Specifies whether apply phase one of the two-phase training (estimation) procedure; use the Phase one algorithm option to select a specific algorithm for this phase.
Phase one algorithm
Specifies the training phase one algorithm; only applicable if the Training phase one parameter is True.
Phase one epochs
Specifies the epochs (number of iterations) for training phase one; only applicable if the Training phase one parameter is True.
Phase one learning rate
Specifies the learning rate for training phase one; only applicable if the Training phase one parameter is True.
Training phase two
Specifies whether apply phase two of the two-phase training (estimation) procedure; use the Phase two algorithm option to select a specific algorithm for this phase.
Phase two algorithm
Specifies the training phase two algorithm; only applicable if the Training phase two parameter is True.
Phase two epochs
Specifies the epochs (number of iterations) for training phase two; only applicable if the Training phase two parameter is True.
Phase two learning rate
Specifies the learning rate for training phase two; only applicable if the Training phase two parameter is True.
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.