Click the *OK*
button in the *SANN
- Analysis/Deployment* Startup Panel to display the *SANN
- Data selection* dialog box, which can contain up to four tabs: *Quick,
Sampling, *Subsampling, and *Time series*. The latter
tab is available for time series analysis. The options described here
are available regardless of which tab is selected.

**OK.**
Click the *OK* button to display the dialog box for the strategy
selected on the Quick
tab (either the *SANN -
Automated Network Search (ANS) *dialog box or the *SANN - Custom Neural Network *dialog
box). Note that if you have not already specified V*ariables*,
a standard variable selection dialog box will be displayed first.

**Cancel.
**Click the *Cancel* button to close the dialog box and
return to the *SANN
- New Analysis/Deployment *Startup Panel.

**Options.**
See Options
Menu for descriptions of the commands on this menu.

**MD
handling (inputs). **Specify how to treat cases with missing
values (in the input variables of the selected models). This group box
is always disabled for Time series analysis. There are two options:

**Casewise.** Any cases
with missing values are omitted when generating results. Cases with missing
target values are labeled as “Missing” and used to form the “Missing”
sample. The missing sample consists of data cases with one or more missing
target values. This option is not available for Time series tasks. Casewise
is the only method available for missing data handling of categorical
variables.

**Mean substitution.**
The mean substitution procedure is used to "patch" missing values
before training or executing the network. When this option is selected,
missing values are replaced with the training sample mean. Note that this
option is applicable only to continuous variables. This implies that the
mean substitution option will be disabled when there are no continuous
inputs in the analysis, and that for classification tasks the option cannot
be applied to the target variable, in which case all cases with missing
targets will be labeled as "missing," which means a case with
missing target value. Such cases are grouped in SANN
as the missing sample and can be used for fixing the basis functions of
the RBF neural networks and for making predictions. Also note that the
mean substitution is not applicable to time series analysis (whether regression
or classification).

**Note: The mean substitution option will always
compute the simple arithmetic mean, to replace missing data, even when
weights are in effect. Weights in SANN are used ("interpreted") as measures
of case "importance", i.e., they will affect the estimation
of neural network parameters themselves. If the intention of weights is
to compute a weighted mean (e.g., a population average computed using
weights) to replace missing data in the input file, use option Data - Data Filtering/Recoding
- Replace
Missing Data replace missing
data values with weighted means.**

**Case
selection.** Click the *Case
selection* button to display the *Analysis/Graph
Case Selection Conditions* dialog box, which is used to create
conditions for which cases will be included (or excluded) in the current
analysis. More information is available in the case selection conditions
overview,
syntax
summary, and dialog box description.

**Case
weights.** Click the *Case weights* button to display the
*Analysis/Graph
Case Weights* dialog box, which is used to adjust the contribution
of individual cases to the outcome of the current analysis by "weighting"
those cases in proportion to the values of a selected variable. In STATISTICA SANN, case weights are used
to encourage a network to emphasis on or ignore learning specific cases
or even regions from the data set. All data cases by default have case
weights equal to 1. If a data case is assigned a case weight less than
1, for example 0.5, then the error due to

Note: Weights in SANN are used and interpreted as measures of case importance, i.e., they will affect the estimation of neural network parameters themselves, but not more. For example, case weights are not used in mean substitution of missing data or calculations of data statistics such as mean and standard deviation of the variables. If you assign weights to cases in the data set, the neural network algorithm will try to predict cases with higher weights with more accuracy. This is useful in a number of situations such as imbalanced data or data sets with cases that are more important to accurately predict. Data cases with zero weights will be excluded from the train, test, and validation samples (i.e., they will be ignored from the analysis). Cases weights can be integers or fractional numbers.