Applies Multilayer Perceptron (MLP) neural network architectures to
classification problems; the final solution is automatically stored for
deployment. MLP neural network architectures with 1, 2, or 3 layers can
be specified, for problems with continuous and/or categorical predictors.

Please note: Multilayer perceptrons with many variables and 2 or 3 layers
can require a substantial amount of training time, even on fast computers
or networks. Always start with simple architectures with few variables
and 1 or 2 layers, before applying more complex models.

**General**

**Detail of
computed results reported**. Detail of computed results; if Minimal
detail is requested, summary statistics for the trained network and a
graph of the network architecture will be displayed; at the Comprehensive
level of detail, sensitivity analysis and descriptive statistics spreadsheets
will be displayed; the All results level will display the predictions
spreadsheets.

**Missing
data**. Specifies the substitution method for missing data.

**Apply memory
limit**. Use this option to limit the maximum data size that
can be processed; note that very large data problems may require significant
memory and processing resources; modify the defaults only as needed.

**Memory limit**.
Use this option to set the maximum data size that can be processed.

**Save/run
network file**. By default (Don't save trained networks), the
program will simply train the network, report the results, and then discard
the trained network. Use the Save network file option to save the trained
network in a specific file for future application to other data; use the
Run network file option to apply a previously saved network to new data.

**Network
file name**. Specifies the name of the network file to save or
run; this option is not applicable if the Save/run network file option
was set to Don't save trained networks.

**Generate
datasource, if N for input less than**. Generate a datasource
for further analyses with other Data Miner nodes if the input datasource
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.

**Units**

**Number of
hidden layers**. Specifies the number of hidden layers for the
multilayer perceptron, and the number of units in each layer.

Please note: Multilayer perceptrons with many variables and 2 or 3 layers
can require a substantial amount of training time, even on fast computers
or networks. Always start with simple architectures with few variables
and 1 or 2 layers, before applying more complex models.

**Number of
units layer 1**. Specifies the number of units for hidden layer
1.

**Number of
units layer 2**. Specifies the number of units for hidden layer
2; not applicable if the Number of hidden layers was selected to be 1.

**Number of
units layer 3**. Specifies the number of units for hidden layer
3; not applicable if the Number of hidden layers was selected to be 2
or less.

**Classification
error**

**Classification
error function**. Specifies classification error function for
output interpretation; applicable to classification-type problems with
categorical output variables.

**Training**

**Training
phase one**. Specifies whether apply phase one of the two-phase
training (estimation) procedure; use the Phase one algorithm option to
select a specific algorithm for this phase.

**Phase one
algorithm**. Specifies the training phase one algorithm; only
applicable if the Training phase one parameter is True.

**Phase one
epochs**. Specifies the epochs (number of iterations) for training
phase one; only applicable if the Training phase one parameter is True.

**Phase one
learning rate**. Specifies the learning rate for training phase
one; only applicable if the Training phase one parameter is True.

**Training
phase two**. Specifies whether apply phase two of the two-phase
training (estimation) procedure; use the Phase two algorithm option to
select a specific algorithm for this phase.

**Phase two
algorithm**. Specifies the training phase two algorithm; only
applicable if the Training phase two parameter is True.

**Phase two
epochs**. Specifies the epochs (number of iterations) for training
phase two; only applicable if the Training phase two parameter is True.

**Phase two
learning rate**. Specifies the learning rate for training phase
two; only applicable if the Training phase two parameter is True.

**Predicted
values**

**Subset to
generate results**. Specifies the subset of observations to be
used to compute predicted and residual values; only applicable if Comprehensive
output or All results are selected as the Detail of computed results reported.

**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.