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

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

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

3. A useful concept in logistic regression is the doubling amount. How would you calculate doubling amount for an input variable that has a parameter estimate of b1?
Response:

A) 2/log(b1)
B) 0.69/b1
C) 2*log(b1)
D) 2*b1

4. Which of the following solves problems for you when you impute missing values?
Response:

A) When you impute a synthetic value, it replaces missing values with 1 or 0.
B) When you impute a synthetic value, each missing value becomes an input to the model.
C) When you impute a synthetic value, it eliminates the incomplete case problem.
D) When you impute a synthetic value, predictive information is retained.

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) 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

Solutions: