Standard Classification Trees with Deployment (C&RT)

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

General

Element Name Description
Detail of computed results reported Detail 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 classifications) are available as an option.
Goodness of fit measure Specifies goodness-of-fit measure for classification; the C&RT-style exhaustive search for univariate splits works by searching for the split that maximizes the reduction in the value of the selected goodness of fit measure. When the fit is perfect, classification is perfect.
Prior class probabilities Specifies how to determine the a-priori classification probabilities.
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 classes Computes observational statistics, including predicted classifications.
Generate datasource, if N for input less than Generates a datasource for further analyses with other Data Miner nodes if the input datasource 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; in V-fold cross-validation random samples are generated from the learning sample to provide an estimate of the CV cost for each classification tree in the tree sequence. 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 Specifies the 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.