Exhaustive Classification CHAID

Full implementation of exhaustive CHAID algorithm (General Chi-square Automatic Interaction Detector) for classification using 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 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 also. Note that observational statistics (predicted classifications) are available as an option.
Minimum n per node Minimum number of observations per node.
Maximum number of nodes Maximum number of nodes.
p value for splitting p value used for splitting.
p value for merging p value used for merging.
Bonferroni adjustment Applies Bonferroni adjustment to probabilities.
Splitting after merging Splitting after merging of categories.
Creates predicted classes Creates observational statistics, including predicted classifications.
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; in V-fold cross-validation random samples are generated from the learning sample; note that in data mining applications with large data sets, V-fold cross-validation may require significant computing time.
Number of folds (sets) Number of folds (sets, random samples) for V-fold cross-validation.
Random number seed Random number seed for V-fold cross-validation (for generating the random samples).

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.