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

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

2. The selected model, based on the misclassification rate for the validation data, has how many input variables?
Response:

A) 2
B) 3
C) 1
D) 4 or more

3. 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) 5.00
B) 67409.93
C) 14.69
D) 5862.76

4. 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.
How many leaves are there in the decision tree?
Response:

A) 21 or more
B) 11-15
C) 1-10
D) 16-20

5. 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

Solutions: