Neural Network Diagram shows a twolayer neural network with three X variables and one Y variable. In this example, the first layer has two nodes, and each node is a function of all three nodes in the second layer. The second layer has three nodes, and all nodes are a function of the three X variables. The predicted Y variable is a function of both nodes in the first layer.
The functions applied at the nodes of the hidden layers are called activation functions. The activation function is a transformation of a linear combination of the X variables. For more details about the activation functions, see Hidden Layer Structure.
Choose a validation column. For more information, see Validation Method. If you click the Validation button with no columns selected in the Select Columns list, you can add a validation column to your data table. For more information about the Make Validation Column utility, see Basic Analysis.


For a continuous variable, missing values are replaced by the mean of the variable. Also, a missing value indicator, named <colname> Is Missing, is created and included in the model. If a variable is transformed using the Transform Covariates fitting option on the Model Launch window, missing values are replaced by the mean of the transformed variable.

After you click Go to fit a model, you can reopen the Model Launch Dialog and change the settings to fit another model.
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The training set is the part that estimates model parameters.

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The validation set is the part that estimates the optimal value of the penalty, and assesses or validates the predictive ability of the model.

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The test set is a final, independent assessment of the model’s predictive ability. The test set is available only when using a validation column. See Validation Methods.

The training, validation, and test sets are created by subsetting the original data into parts. Validation Methods describes several methods for subsetting a data set.
The functions applied at the nodes of the hidden layers are called activation functions. An activation function is a transformation of a linear combination of the X variables. Activation Functions describes the three types of activation functions.
where x is a linear combination of the X variables.


where x is a linear combination of the X variables.

The learning rate must be 0 < r ≤ 1. Learning rates close to 1 result in faster convergence on a final model, but also have a higher tendency to overfit data. Use learning rates close to 1 when a small Number of Models is specified.
Fitting Options describes the model fitting options that you can specify.
The penalty is , where λ is the penalty parameter, and p( ) is a function of the parameter estimates, called the penalty function. Validation is used to find the optimal value of the penalty parameter.