Creates boosting regression trees with deployment for continuous and categorical predictors. Various observational statistics (predicted values, residuals) can be requested as an option.

**General**

**Detail of
computed results reported.**. Selects the detail of the reported
results; if Minimal detail is selected, the program will only report the
summary statistics from the analysis; if Comprehensive detail is requested,
the tree graph and tree structure will also be displayed; if All results
are requested, various graphs and spreadsheets of predicted values (regression)
and statistics will also be reported.

**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.

**Advanced**

**Learning rate**. Specify the learning or shrinkage rate for the
computations. The Statistica Boosting Trees module will compute
a weighted additive expansion of simple regression trees. The specific
weight with which consecutive simple trees are added into the prediction
equation is usually a constant, and referred to as the learning rate or
shrinkage parameter; empirical studies have shown that shrinkage values
of .1 or less usually lead to better models (with better predictive validity).

**Number of
additive trees**. Specify the number of additive terms to be
computed, i.e., the number of simple regression trees to be computed in
successive boosting steps.

**Subsample
proportion**. Specify the subsample proportion to be used for
drawing the random learning sample for consecutive boosting steps.

**Random test
data proportion**. Specify here the proportion of randomly chosen
observations that will serve as a test sample in the computations; this
option is only applicable, if the Test sample option is set to Off.

**Minimum
number to stop**. One way to control splitting is to allow splitting
to continue until all terminal nodes contain no more than a specified
minimum number of cases or objects; this minimum number of cases in a
terminal node can be specified via this option.

**Minimum
child node size to stop**. Use this option to control the smallest
permissible number in a child node, for a split to be applied. While the
Minimum n of cases parameter determines whether an additional split is
considered at any particular node, the Minimum n in child node parameter
determines whether a split will be applied, depending on whether any of
the two resultant child nodes will be smaller (have fewer cases) than
n as specified via this option.

**Maximum
number of levels**. The value entered here will be used for stopping
on the basis on the number of levels in a tree. Each time a parent node
is split, the total number of levels (depth of the tree as measured from
the root node) is examined, and the splitting is stopped if this number
exceeds the number specified in the Maximum n levels box.

**Maximum
number of nodes**. The value entered here will be used for stopping
on the basis of the number of nodes in each tree. Each time a parent node
is split, the total number of nodes in the tree is examined, and the splitting
is stopped if this number exceeds the number specified in Maximum n nodes
box. The default value 3 would cause each consecutive tree to consist
of a single split (one root node, two child nodes).

**Seed for
random number generator**. Specify a constant for seeding the
random number generator, which is used to select the subsamples for consecutive
boosting trees.

**User-defined
final model**. Set this option to TRUE in order to select a particular
model (with a particular number of consecutive trees) as the final model.
By default (FALSE), the program will automatically select the model that
generated the smallest cross-validation error in the test sample. If you
set this option to FALSE, specify the desired (final) number of trees
in Number of trees for model option below.

**Number of
trees for model**. This option is only applicable if the User-defined
final model option is set to TRUE. In that case specify the desired (final)
number of trees. By default (User-defined final model = FALSE), the program
will automatically select the model that generated the smallest cross-validation
error in the test sample.

**Results**

**Start tree
number**. The method of stochastic gradient boosting trees will
generate a sequence of simple trees (the complexity of each tree can be
specified). If you want to review the actual individual trees (as Tree
graphs or the Tree structure), use the Start/End tree number to select
the specific numbers of trees you want to review.

**End tree
number**. The method of stochastic gradient boosting trees will
generate a sequence of simple trees (the complexity of each tree can be
specified). If you want to review the actual individual trees (as Tree
graphs or the Tree structure), use the Start/End tree number to select
the specific numbers of trees you want to review.

**Predictions
for all samples**. Computes predicted values and other statistics
for all observations (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.