 The data that is used to construct this plot is ordered by expected profit. For this
example, you have defined a profit matrix. Therefore, expected profit is a function of
both the probability of donation for an individual and the profit associated with the
corresponding outcome. A value is computed for each decision from the sum of the
decision matrix values multiplied by the classification probabilities and minus any
defined cost. The decision with the greatest value is selected, and the value of that
selected decision for each observation is used to compute overall profit measures.
The plot represents the cumulative total expected profit that results from soliciting
the best n% of the individuals (as determined by expected profit) on your mailing
list. For example, if you were to solicit the best 40% of the individuals, the total
expected profit from the validation data would be approximately \$1850. If you were
to solicit everyone on the list, then based on the validation data, you could expect
approximately \$2250 profit on the campaign.
10. Close the Results window.
Analyze with a 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.
36 Chapter 6 Impute and Transform, Build Neural Networks, and Build a Regression Model 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.
This example does not use the AutoNeural node. However, you are encouraged to
explore the features of this node on your own.
Before creating a neural network, you will reduce the number of input variables with the
Variable Selection node. Performing variable selection reduces the number of input
variables and saves computer resources. To use the Variable Selection node to reduce the
number of input variables that are used in a neural network:
1. Select the Explore tab on the Toolbar.
2. Select the Variable Selection node icon. Drag the node into the Diagram Workspace.
3. Connect the Transform Variables node to the Variable Selection node.
4. In the Diagram Workspace, right-click the Variable Selection node, and select Run
from the resulting menu. Click Yes in the Confirmation window that opens.
5. In the window that appears when processing completes, click Results. The Results
window appears.
6. Expand the Variable Selection window.
Analyze with a Neural Network Model 37 Examine the table to see which variables were selected. The role for variables that
were not selected has been changed to Rejected. Close the Results window.
Note: In this example, for variable selection, a forward stepwise least squares
regression method was used. It maximizes the model R-square value. For more