Analyze with a User-Specified Neural Network Model

Neural networks are a class of parametric models that can accommodate a wider variety of nonlinear relationships between a set of predictors and a target variable than can logistic regression. Building a neural network model involves two main phases. First, you must define the network configuration. You can think of this step as defining the structure of the model that you want to use. Then, you iteratively train the model.

A neural network model will be more complicated to explain to the management of your organization than a regression or a decision tree. However, you know that the management would prefer a stronger predictive model, even if it is more complicated. So, you decide to run a neural network model, which you will compare to the other models later in the example.

Because neural networks are so flexible, SAS Enterprise Miner has two nodes that fit neural network models: the Neural Network node and the AutoNeural node. The Neural Network node trains a specific neural network configuration; this node is best used when you know a lot about the structure of the model that you want to define. The AutoNeural node searches over several network configurations to find one that best describes the relationship in a data set and then trains that network.

To use the Neural Network node to train a specific neural network configuration, complete the following steps:

From the Model tab on the Toolbar, select the Neural Network node icon. Drag the node into the Diagram Workspace.
Connect the Transform Variables node to the Neural Network node.
Select the Neural Network node. In the Properties Panel, scroll down to view the Train properties, and click on the ellipses that represent the value of Network. The Network window opens.
1. Click on the value of Direct Connection and select Yes from the drop-down menu that appears. This selection enables the network to have connections directly between the inputs and the outputs in addition to connections via the hidden units.
2. Click on the value of Number of Hidden Units and enter 5. This example trains a multilayer perceptron neural network with five units on the hidden layer.
3. Click OK .
In the Diagram Workspace, right-click the Neural Network node, and select Run from the resulting menu. Click Yes in the confirmation window that opens.
In the window that appears when processing completes, click Results. The Results window opens. Maximize the Score Rankings Overlay window. From the drop-down menu, select Cumulative Total Expected Profit.

Notice that the plot from this model has the same shape as the plot from the logistic regression model.
Close the Results window.