Standard Regression Trees (C&RT) with Deployment

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


Detail of computed results reported. Details of computed results; if Minimal results are requested, then only the final tree will be displayed; if Comprehensive detail is requested, then various other statistical summaries are reported as well; if All results are requested, then 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.

Computes predicted values. Computes observational statistics (predicted values).

Generate 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 Crossvalidation

V-fold crossvalidation. Performs V-fold crossvalidation; note that in data mining applications with large data sets, V-fold crossvalidation may require significant computing time.

Number of folds(sets). Specifies the number of folds (sets, random samples) for V-fold crossvalidation.

Random number seed. Specifies the random number seed for V-fold crossvalidation (for generating the random samples).

Standard error rule. Specifies the standard error rule for finding optimal trees via V-fold crossvalidation; refer to the Electronic Manual for additional details.

Deployment. Deployment is available if the Statistica installation is licensed for this feature.

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.