Standard Regression Trees (C&RT)

Creates standard regression trees (C&RT) for continuous and categorical predictors; builds an optimal tree structure to predict continuous dependent variables via V-fold cross-validation (optional). Various observational statistics (predicted values) can be requested as an option.

General

Element Name Description
Detail of computed results reported Specifies the detail of computed results reported. If Minimal results is requested, only the final tree will be displayed; if Comprehensive detail is requested, various other statistical summaries are reported as well; if All results is requested, various node statistics and graphs are reported as well. Note that observational statistics (predicted values) are available as an option.
Stopping option for pruning Specifies the stopping rule for the pruning computations.
Minimum n per node Specifies a minimum n-per-node, when pruning should begin; this value controls when split selection stops and pruning begins.
Fraction of objects Specifies the fraction of object for FACT-style direct stopping.
Maximum number of nodes Specifies the maximum number of nodes.
Number of surrogates Specifies the number of surrogates for surrogate splits.
Creates predicted classes Creates observational statistics (predicted values).
Generates data source, if N for input less than Generates 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.

V-Fold Cross-Validation

Element Name Description
V-fold cross-validation Performs V-fold cross-validation; note that in data mining applications with large data sets, V-fold cross-validation may require significant computing time.
Number of folds(sets) Specifies the number of folds (sets, random samples) for V-fold cross-validation.
Random number seed Specifies the random number seed for V-fold cross-validation (for generating the random samples).
Standard error rule Standard error rule for finding optimal trees via V-fold cross-validation; refer to the Electronic Manual for additional details.

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.