Create a Data Source
To use sample data that is stored in a SAS data set in a SAS Enterprise Miner project,
you need to define a data source. In SAS Enterprise Miner, a data source stores the
metadata of an input data set.
T I P You can also use input data saved in files (with extensions such as .jmp and .csv)
that are not SAS data sets in a process flow. To import an external file into a process
flow diagram, use the File Import node, which is located on the Sample tab on the
Toolbar.
To create a new data source for the sample data:
1. On the File menu, select New ð Data Source. The Data Source Wizard opens.
2. Proceed through the steps that are outlined in the wizard.
a. SAS Table is automatically selected as the Source. Click Next.
b. Enter DONOR.DONOR_RAW_DATA as the two-level filename of the Table. Click
Next.
c. Click Next.
d. Select the Advanced option button. Click Next.
e. Change the value of Role for the variables to match the description below. Then,
click Next.
CONTROL_NUMBER should have the Role ID.
TARGET_B should have the Role Target.
TARGET_D should have the Role Rejected.
All other variables should have the Role Input.
To change an attribute, click on the value of that attribute and select from the
drop-down menu that appears.
Create a Data Source 11
Note: SAS Enterprise Miner automatically assigns the role Target to any
variable whose name begins with the prefix TARGET_. For more
information about the rules that SAS Enterprise Miner uses to automatically
assign roles, see the SAS Enterprise Miner Help.
f. Select the Yes option button to indicate that you want to build models based on
the values of decisions. Click Next.
On the Prior Probabilities tab, select the Yes option button to indicate that
you want to enter new prior probabilities. In the Adjusted Prior column of the
table, enter 0.05 for Level 1 and 0.95 for Level 0.
The values in the Prior column reflect the proportions of observations in the
data set for which TARGET_B is equal to 1 and 0 (0.25 and 0.75,
respectively). However, as the business analyst, you know that these
proportions resulted from over-sampling of donors from the 97NK
solicitation. In fact, you know that the true proportion of donors for the
solicitation was closer to 0.05 than 0.25. For this reason, you adjust the prior
probabilities.
On the Decision Weights tab, the Maximize option button is automatically
selected, which indicates that you want to maximize profit in this analysis.
Enter 14.5 as the Decision 1 weight for Level 1, -0.5 as the Decision 1
weight for Level 0, and
0.0 as the Decision 2 weight for both levels. Click
Next.
In this example, Decision 1 is the decision to mail a solicitation to an
individual. Decision 2 is the decision to not mail a solicitation. If you mail a
solicitation, and the individual does not respond, then your cost is $0.50 (the
price of postage). However, if the individual does respond, then based on the
previous solicitation, you expect to receive a $15.00 donation on average.
Less the $0.50 postage cost, your organization expects $14.50 profit. If you
do not mail a solicitation, you neither incur a cost nor expect a profit. These
numbers are reflected in the decision weights that you entered in the table.
Click Next.
g. In the Data Source Wizard — Create Sample window, you decide whether to
create a sample data set from the entire data source. This example uses the entire
data set, so you need to select No. Click Next.
h. The Role of the data source is automatically selected as Raw. Click Next.
i. Click Finish.
12 Chapter 3 Set Up the Project

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