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

1. 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

2. Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
For the validation data, in what range does cumulative percent captured response at the 60th percentile lie?
Response:

A) 25-49.99
B) 0-24.99
C) 50-74.99
D) 75 or more

3. Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:

A) Decision Tree
B) Neural Network
C) Ensemble
D) Regression

4. What is the kurtosis value for the variable TLDel60Cnt24?
Response:

A) between 10 and 13.99
B) less than 10
C) 17 or higher
D) between 14 and 16.99

5. Suppose your input variables have missing values. Before running a decision tree with these input variables, you should do which of the following?
Response:

A) impute only class variables using the Tree method but do not impute the interval variables
B) not impute any missing values because trees can handle them.
C) impute only interval variables using the Tree method but do not impute the class variables
D) impute all missing values using the Tree method

Solutions: