Principal Components Network
Principal components networks are simply linear networks used to perform Principal Component Analysis (PCA). PCA is useful to reduce the dimensionality of data sets with many input variables.
General
Element Name | Description |
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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, the eigenvalue spreadsheet of the principal components will be displayed; the All results level will display the predictions spreadsheet. |
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. |
Deployment
Deployment is available if the Statistica installation is licensed for this feature.
Element Name | Description |
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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. |
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