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

1. Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

A) The optimization for the model has not been completed.
B) The misclassification error for the test data is 0.154255.
C) All of the above
D) The model has too few input variables.

2. For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:

A) It looks for a set of colinear inputs that correlate with the target.
B) It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.
C) It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
D) It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.

3. If you only consider observations for which TARGET=0, what percentage of such observations has BanruptcyInd=1?
Response:

A) between 15%-49.99%
B) less than 15%
C) between 50%-79.99%
D) 80% or higher

4. The number of hidden layers in this Neural Network model is which of the following?
Response:

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

5. Sometimes in predictive modeling we build models using a sample that has a primary outcome proportion different from true population proportion. This is usually done when the ratio of primary to secondary outcomes in a binary target variable in the population is close to which of the following?
Response:

A) 1
B) 1.2
C) 0.05
D) 0.8

Solutions: