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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q58-Q63):

NEW QUESTION # 58
What is the number of missing values for the TLSum variable in the sample generated by SAS Enterprise Miner?
Response:

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

Answer: B

NEW QUESTION # 59
You are building a model to identify fraud. Your model will produce predictions that can be interpreted as the probability of fraud. You will pass on the top 100 scoring cases to management for investigation. Assume that you have sufficient data to hold out a validation and test data set for model evaluation.
Which selection would represent a reasonable ordering of fit statistics (best to worst) for this situation?
Select one:
Response:

A. ROC index, ASE, misclassification rate
B. ASE, Lift for the top 10%, ROC index
C. R-square, AIC, K-S statistic
D. Misclassification rate, ROC index, ASE

Answer: A

NEW QUESTION # 60
Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

A. The optimization for the model has not been completed.
B. The misclassification error for the test data is 0.154255.
C. All of the above
D. The model has too few input variables.

Answer: A

NEW QUESTION # 61
Which of the following is not true about results produced by the Regression node?
Response:

A. Variable Summary information identifies the roles of variables used by the Regression node.
B. Model Information provides you with information that includes the number of target categories and the number of model parameters.
C. Fit Statistics can provide information that affects decision predictions, but does not affect estimate predictions.
D. Type 3 Analysis of Effects provides you with information about the number of parameters that each input contributes to the model.

Answer: C

NEW QUESTION # 62
1. Define a new data source, PatternData, in SAS Enterprise Miner (SAS data set Patterndata.sas7bdat in the zip file distributed with this practice exam).
2. Set the role of all variables to Input, with the exception set the ID variable role to ID.
3. Set the measurement level for all variables to Interval, except:
- Set DemHomeOwner and StatusCatStarAll to Binary.
- Set DemCluster, DemGender, ID, and StatusCat96NK to Nominal.
4. Create a new diagram (name it Section6) within the project labeled Test.
5. Add the data source, PatternData, to this diagram. Make sure the variable roles and measurements are the same as in the table below. (Check the highlighted rows carefully and reset roles/levels as needed.)
6. Connect a Cluster node to the data source.
7. Modify the Cluster node to exclude nominal and binary input variables.
8. Run the Cluster node.

How many clusters are created by the Cluster node?
Response:

A. 0
B. 1
C. 2
D. 3

Answer: C

NEW QUESTION # 63
......

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