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

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

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

2. 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.
In the validation data, the lift corresponding to the fourth decile is in which of the following ranges?
Response:

A) 1.75 or more
B) 1.5-1.74999
C) 1.25-.49999
D) 0-1.24999

3. Perform these tasks in SAS Enterprise Miner:
* Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT dat a. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:

The distribution of the predicted probabilities of TARGET=0 in the scoring data is approximately which of the following?
Response:

A) left skewed
B) bimodal
C) right skewed
D) normal

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.
What is the probability that TARGET=0 for ID=000355 in the training data?
Response:

A) 0.9220647773
B) 0.077935227
C) 0.0658174098
D) 0.9341825902

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

Solutions: