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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 Importance statistic for MTGBal (Mortgage Balance)?
Response:

A) 0.42541
B) 0.32959
C) 0.60485
D) 0.42667

2. 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.
Suppose that the data has been oversampled and the probability that TARGET=1 is 0.10 in the population. Incorporate the above scenario and run the Decision Tree node again.
What is the misclassification rate in the validation data set?
Response:

A) 0.10
B) 0.154788
C) 0.157016
D) 0.162252

3. Which of the following is not a good reason to"regularize" input distributions using a simple transformation?
Response:

A) One benefit is improved model performance.
B) When you perform regression, inputs with highly skewed or highly kurtotic distributions can be selected over inputs that would yield better overall predictions.
C) Regression models are sensitive to extreme or outlying values in the input space.
D) Another benefit is ease in model interpretation.

4. What is the number of missing values for the TLSum variable in the sample generated by SAS Enterprise Miner?
Response:

A) 1-19
B) 20-39
C) 40 or more
D) 0

5. What is the average squared error in the training data?
Response:

A) 0.131709
B) 0.131583
C) 0.133665
D) 0.131208

Solutions: