Workspace Node: SANN Regression - Specifications - Quick Tab

The SANN Regression workspace node can be accessed from the Feature Finder, the ribbon bar, or the Node Browser. The Specifications - Quick tab is displayed by default when the specifications dialog box is opened.

Element Name Description
Use previous input description Select this check box to use the variable selection specified in the Select dependent variables and predictors node, accessed via the ribbon bar or by double-clicking the data source node in the workspace. Note that the previous Select Cases and Weight specifications will be used also. If case selection conditions and/or weights were previously specified, you can view those specifications from this workspace dialog box, but you cannot change them (the options will be dimmed).
Variables Click this button to display a variable selection dialog box. For Regression, you will be able to select Continuous targets and Continuous and/or Categorical inputs. This button is not available when the Use previous input description check box is selected.
Analysis variables (present in the dataset) This group box displays the variables currently selected for the analysis.
Strategy for creating predictive models Different tabs and options will be available according to the option selected in this group box.
Automated network search (ANS) Select this option button to enable Automated Network Search (ANS), which is used for creating neural networks with various settings and configurations with minimal effort. ANS helps you create and test neural networks for your data analysis and prediction problems. It designs a number of networks to solve the problem and then selects those networks that best represent the relationship between the input and target variables. ANS is not applicable when deploying models since no training of neural networks is required.
Custom neural networks (CNN) Select this option button to use the Custom neural networks (CNN) strategy. In contrast to ANS, with the Custom Neural Networks (CNN) tool, you can choose individual network architectures and training algorithms to exact specifications. You can use CNN to train multiple neural network models with exactly the same design specifications but with different random initialization of weights. As a result, each network will find one of the possible solutions posed by neural networks of the same architecture and configurations.
Subsampling (random, bootstrap) Select this option button to use the Subsampling (random, bootstrap) strategy. This tool enables you to create an ensemble of neural networks based upon multiple subsamples of the original data set. Options for the Subsampling (random, bootstrap) strategy are available on the Subsampling tab.
MD handling (inputs) Specify how to treat cases with missing values (in the input variables of the selected models).
Casewise When this option button is selected, 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.
Mean substitution The mean substitution procedure is used to "patch" missing values before training or executing the network. When this option button is selected, missing values are replaced with the training sample mean. Note that this option is applicable only to continuous variables, i.e., the Mean substitution option button 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.
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 - Filter/Recode - Process Missing Data to replace missing data values with weighted means.

Options / C / W. See Common Options.

OK Click the OK button to accept all the specifications made in the dialog box and to close it. The analysis results will be placed in the Reporting Documents node after running (updating) the project.

See also, Specifications - Sampling (CNN and ANS) tab, Specifications - Subsampling tab, Specifications - Quick Specification tab, Specifications - MLP Activation Functions tab, MLP tab, Specifications - Weight Decay tab, Specifications - Initialization tab, Results - Samples tab, Results - Predictions tab, Results - Graphs tab, Results - Details tab, Results - Code Generator tab, Downstream tab, and Home tab.