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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:

1. Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:

1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Cubic Clustering Criterion statistic for this clustering?
Response:

A) 14.69
B) 67409.93
C) 5.00
D) 5862.76

2. How many hidden layers are generally needed in an MLP-based neural network to capture a discontinuous relationship between inputs and target?
Response:

A) one hidden layer
B) no hidden layer, direct connection between inputs and output is preferred
C) two hidden layers
D) three or more hidden layers

3. Perform these tasks in SAS Enterprise Miner:
Add a Decision Tree node, as shown below. (Make sure you use only default options in the Decision Tree node.)

Run the Decision Tree node.
Now suppose that the bank expects to make a profit of $200 USD when TARGET=1, but it expects to lose $25 USD when TARGET=0. Incorporate the above scenario, change the assessment measure of the decision tree to average square error, and then run the Decision Tree node. What is the total profit for the test data set?
Response:

A) 300-999
B) less than or equal to 299
C) 1,600 or higher
D) 1,000-1,599

4. Assume the Target has an event proportion of 2% in the original data. Which of the following property values should be used in the Sample node of SAS Enterprise Miner to create a sample from that data with a balanced 50/50 split for Target?
Select one:
Response:

A) Sample Method: Stratify and Criterion: Proportional
B) Sample Method: Stratify and Criterion: Equal
C) Sample Method: Random and Criterion: Equal
D) Sample Method: Random and Criterion: Proportional

5. How many unique indicator variables were created during imputation?
Response:

A) 1-10
B) none
C) 21 or higher
D) 11-20

Solutions: