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

1. 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) normal
C) right skewed
D) bimodal

2. Which model was picked as the best model by SAS Enterprise Miner?
Response:

A) None of the above
B) Regression
C) Decision Tree
D) Decision Tree (3-way)

3. Assume a variable is coded as follows: 1=unmarried, 2=married, 3=divorced, and 4=widowed. Then which of the following measurement levels should be selected in SAS Enterprise Miner for this variable?
Response:

A) Interval
B) Ordinal
C) Nominal
D) Unary

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

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

5. What is the number of missing values for the TLSum variable in the sample generated by SAS Enterprise Miner?
Response:

A) 0
B) 20-39
C) 40 or more
D) 1-19

Solutions: