T I P You can hold down the Ctrl key to select multiple rows. Then, when you
select a new Method for one of the selected variables, the new method will
apply to all of the selected variables.
b. Select the transformation Method for the following interval variables and select
Optimal Binning from the drop-down menu that appears:
The optimal binning transformation is useful when there is a nonlinear
relationship between an input variable and the target. For more information about
this transformation, see the SAS Enterprise Miner Help.
c. Click OK.
5. In the Diagram Workspace, right-click the Transform Variables node, and select Run
from the resulting menu. Click Yes in the Confirmation window that opens.
6. In the window that appears when processing completes, click OK.
Note: In the data that is exported from the Transform Variables node, a new variable is
created for each variable that is transformed. The original variable is not overwritten.
Instead, the new variable has the same name as the original variable but is prefaced
with an identifier of the transformation. For example, variables to which the log
transformation have been applied are prefaced with LOG_, and variables to which
the optimal binning transformation have been applied are prefaced with OPT_. The
original version of each variable also exists in the exported data and has the role
Analyze with a Logistic Regression Model
As part of your analysis, you want to include some parametric models for comparison
with the decision trees that you built in Chapter 5, “Build Decision Trees,” on page 21.
Because it is familiar to the management of your organization, you have decided to
include a logistic regression as one of the parametric models.
To use the Regression node to fit a logistic regression model:
1. Select the Model tab on the Toolbar.
2. Select the Regression node icon. Drag the node into the Diagram Workspace.
3. Connect the Transform Variables node to the Regression node.
Analyze with a Logistic Regression Model 33
4. To examine histograms of the imputed and transformed input variables, right-click
the Regression node and select Update. In the diagram workspace, select the
Regression node. In the Properties Panel, scroll down to view the Train properties,
and click on the ellipses that represent the value of Variables. The Variables — Reg
a. Select all variables that have the prefix LG10_. Click Explore. The Explore
34 Chapter 6 • Impute and Transform, Build Neural Networks, and Build a Regression Model
You can select a bar in any histogram, and the observations that are in that bucket
are highlighted in the EMWS.Trans_TRAIN data set window and in the other
histograms. Close the Explore window to return to the Variables — Reg window.
b. (Optional) You can explore the histograms of other input variables.
c. Close the Variables — Reg window.
5. In the Properties Panel, scroll down to view the Train properties. Click on the
Selection Model property in the Model Selection subgroup, and select Stepwise
from the drop-down menu that appears. This specification causes SAS Enterprise
Miner to use stepwise variable selection to build the logistic regression model.
Note: The Regression node automatically performs logistic regression if the target
variable is a class variable that takes one of two values. If the target variable is a
continuous variable, then the Regression node performs linear regression.
6. In the Diagram Workspace, right-click the Regression node, and select Run from the
resulting menu. Click Yes in the Confirmation window that opens.
7. In the window that appears when processing completes, click Results. The Results
8. Maximize the Output window. This window details the variable selection process.
Lines 401 – 424 list a summary of the steps that were taken.
9. Minimize the Output window and maximize the Score Rankings Overlay window.
From the drop-down menu, select Cumulative Total Expected Profit.
Analyze with a Logistic Regression Model 35