General Classification CHAID

Full implementation of the standard CHAID algorithm (General Chi-square Automatic Interaction Detector) for classification based on coded (general) ANCOVA-like designs 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 is 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 is requested, then various node statistics and graphs are also reported. Note that observational statistics (predicted classifications) are available as an option.
Analysis syntax Analysis syntax string for General Classification CHAID. You can specify here the complete syntax, as, for example, copied from a Statistica analysis. Set this string to empty, or just TREES; to create the syntax from the specific options specified below.
Search method Specifies a method for finding the optimal split for each node.
Design Specifies the design for the between group (ANCOVA-like) design (categorical and continuous predictors); by default (if no design is specified) a full factorial design will be constructed for categorical predictors, and continuous predictor main effects are evaluated.

 Use the syntax:
 DESIGN = Design specifications

 Example 1.
 DESIGN = GROUP | GENDER | TIME | PAID; {makes a full factorial design}

 Example 2.
 DESIGN = SEQUENCE + PERSON(SEQUENCE) + TREATMNT + SEQUENCE*TREATMNT;

 Example 3.
 DESIGN = MULLET | SHEEPSHD | CROAKER @2; {Makes factorial design to degree 2}

 Example 4.
 DESIGN = TEMPERAT | MULLET | SHEEPSHD | CROAKER - TEMPERAT; {Removes main effect for TEMPERAT from factorial design}

 Example 5.
 DESIGN = BLOCK + DEGREES + DEGREES*DEGREES + TIME + TIME*TIME + TIME*DEGREES;
Parameterization Specifies the parameterization of the ANCOVA-like between group design for the categorical predictors; specify No parameterization to perform a regular CHAID analyses (where categorical predictors are not coded into design vectors); for additional details, see the Electronic Manual topic The Sigma-Restricted vs. Overparameterized Model.
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.

Stopping Parameters

Element Name Description
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.

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.