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.

**General**

**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.