Linear

Linear Neural Networks implement a basic linear model, used principally for regression (although they can be used for classification, forming a simple linear discriminant). Linear models are equivalent to simple forms of neural network, with no hidden layer.

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.

Pruning

Element Name Description
Prunes inputs and units Specifies whether input variables, hidden units, or both should be pruned, and the threshold at which pruning takes place.
Prunes inputs Prunes input variables; only applicable if Prune inputs and units is selected.
Prunes hidden units Prunes hidden input units; only applicable if Prune inputs and units is selected.
Threshold value Specifies a threshold value for pruning; only applicable if pruning is elected.
Prunes inputs low Specifies sensitivity-analysis based pruning after training. A sensitivity analysis is run after the network is trained, and input variables with training and selection sensitivity ratios below the threshold (see below) are pruned.
Ratio value Specifies the sensitivity pruning ratio threshold; only applicable if Prune inputs low is selected.

Classification

Element Name Description
Classification thresholds Specifies how to assign cases for classification.
Accept value Specifies the accept thresholds if the option to explicitly specify thresholds is selected.
Reject value 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.