Full implementation of standard exhaustive CHAID algorithm (General Chi-square Automatic Interaction Detector) for predicting a continuous dependent variable based on 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**

**Detail of
computed results reported**. Specifies the 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 also. Note that observational
statistics (predicted values) are available as an option.

**Minimum
n per node**. Specifies the minimum number of observations per
node.

**Maximum
number of nodes**. Specifies the maximum number of nodes.

**p value
for splitting**. Specifies the p value used for splitting.

**p value
for merging**. Specifies the p value used for merging.

**Bonferroni
adjustment**. Applies Bonferroni adjustment to probabilities.

**Splitting
after merging**. Specifies the splitting after merging of categories.

**Computes
predicted values**. Computes observational statistics, including
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 Cross-Validation**

**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)**. 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).

**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.