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

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

2. Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

A) increase the performance of logistic regression
B) neither increase nor decrease the performance of logistic regression
C) decrease the performance of logistic regression
D) create convergence problems in maximum likelihood estimation

3. Refer to the exhibit:

The SAS data set credit_customers contains a numeric variable units_sold that holds only the values: 1, 2, 3, 4. Based on the settings provided in the Advanced Advisor Options, what will be the Role and Level of the units_sold variable when the credit_customers data set is created using Advanced Metadata Advisor in the Data Source Wizard?
Select one:
Response:

A) Role: RejectedLevel: Nominal
B) Role: InputLevel: Nominal
C) Role: InputLevel: Interval
D) Role: IntervalLevel: Input

4. 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.
In the decision tree model, what is the importance of the variable InqCnt06?
Response:

A) 0.45 or higher
B) less than 0.149999
C) 0.15-0.299999
D) 0.30-0.449999

5. Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
The number of input variables being used by the Neural Network model is which of the following?
Response:

A) 11-15
B) 21 or more
C) 16-20
D) less than or equal to 10

Solutions: