Full-featured implementation of Support Vector Machines (SVM) for regression problems. The final solution is automatically stored for deployment.

**General**

**Detail of
computed results reported**. Detail of computed results; if Minimal
detail is requested, spreadsheets of analysis summary, model specifications
as well descriptive statistics (regression statistics) will be displayed;
at the Comprehensive level of detail, a spreadsheet of predictions and
residuals as well as their histogram plots will be displayed; in addition
to the above, the All results level will display a spreadsheet (if the
'Creates residual statistics' option is selected) containing all data
set variables and their statistics including predictions and residuals/accuracy
(whichever applicable).

**Missing
data deletion**. Specifies the substitution method for missing
data. Casewise excludes cases that contain any missing data for any of
the selected variables in the analysis. Mean substitution replaces missing
data by the means for the respective variables (Note: This option is not
applicable for categorical dependent and predictor variables)

**Generate
datasource, if N for input less than**. Generate 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.

**Sampling**

**Divide data
into train and test samples**. Divides the data set into training
and test sample. The training subset is used to fit the model while the
test subset serves as an independent check of its performance

**Sampling method**. Sampling method to be used for dividing the
data set into train and test subsets. Random sampling will divide the
data set into train and testing samples in a random fashion. This is in
contrast to the First N method which selects the first N cases as the
training set and the rest as the testing sample. NOTE: you may also use
a learning/testing indicator variable method for sampling from the data.
You can access this functionality via the Advanced tab of the data spreadsheet
in the Data Acquisition of Statistica Data Miner environment.
Selecting this method (i.e. learning/testing indicator) will override
any choice of sampling you make on this tab.

**Size of
training sample**. Specifies the percentage of data cases that
will be used to form the training sample. The remaining valid cases in
the data set will be used as the test sample.

**Seed**.
Specifies the random generator seed for random sampling of data into train
and test subsets

**Use first
N cases**. Selects the first N valid cases in the data set as
training subset. The rest are used for testing

**SVM**

**SVM type**.
Specifies the type of the SVM model

**Capacity**.
Capacity parameter

**Epsilon**.
Epsilon parameter

**Nu**.
Nu parameter

**Kernel**

**Kernel type**. Specifies the type of
the Kernel used by the SVM model

**Degree**.
Specifies the degree of the polynomial kernel.

**Gamma**.
Specifies the gamma parameter for polynominal, RBF and sigmoid kernels.

**Coefficient**.
Specifies the coefficient for polynominal and sigmoid kernels.

**Cross-validation
1**

**Apply v-fold
cross-validation**. Applies v-fold cross-validation to obtain
estimates of the capacity, epsilon and nu parameters

**V value**.
Number of cross-validation folds

**Seed**.
Seed value for random data shuffling for cross-validation

**Minimum
C**. Start value for the capacity parameter (used by cross-validation
grid search)

**Maximum
C**. End value for the capacity parameter (used by cross-validation
grid search)

**Increment
in C**.

**Minimum
Epsilon**. Start value for the epsilon parameter (used by cross-validation
grid search)

**Maximum
Epsilon**. End value for the epsilon parameter (used by cross-validation
grid search)

**Epsilon
increment**. Increment in epsilon

**Cross-validation
2**

**Minimum
Nu**. Start value for the nu parameter (used by cross-validation
grid search)

**Maximum
Nu**. End value for the epsilon parameter (used by cross-validation
grid search)

**Nu increment**.
Increment epsilon (used by cross-validation grid search)

**Training**

**Max number
of iterations**. The maximum number of iterations that can be
applied in training the SVM model

**Stop at
accuracy**. Training stops when the given level of accuracy is
reached

**Cache size,
in MB**. Cache size in MB

**Shrink data**.
Shrink data

**Scale inputs**.
Check this option to linearly scale the inputs within the range 0 to 1

**Scale outputs**.
Check this option to linearly scale the outputs within the range -1 to
1

**Results**

**Subset used
to generate results**. Select the subset for which the results
should be displayed

**Include
inputs**. Includes the independent variables in spreadsheets
and histograms.

**Include
outputs**. Includes the dependent variables in spreadsheets and
histograms.

**Include
predictions**. Includes predictions in spreadsheets and histograms.

**Include
residuals**. Includes residuals in spreadsheets and histograms.

**Creates
residual statistics**. Creates predicted and residual statistics
for each case depending on the selected level of 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.