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

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

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

3. 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) 16-20
B) 21 or more
C) 11-15
D) 1-10

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.
The number of parameters (weights) estimated by the Neural Network model is in which of the following ranges?
Response:

A) less than or equal to 5
B) 11-15
C) 6-10
D) 16 or more

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

A) Regression models are sensitive to extreme or outlying values in the input space.
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) Another benefit is ease in model interpretation.
D) One benefit is improved model performance.

Solutions: