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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q59-Q64):

NEW QUESTION # 59
For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:

A. Square Root
B. Exponential
C. Log
D. Square

Answer: A

NEW QUESTION # 60
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 percentage of TARGET=1 as predicted by the best model on the scoring data is in which of the following ranges?
Response:

A. under 4.99%
B. 6%-6.99%
C. 7% or higher
D. 5%-5.99%

Answer: A

NEW QUESTION # 61
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. 0.0658174098
C. 0.9341825902
D. 0.077935227

Answer: A

NEW QUESTION # 62
Refer to the exhibit:

When the Explore button is selected in the graphic, what information will be displayed?
Select one:
Response:

A. Sample Properties, Sample Statistics, sample data for the selected variable, and a pie chart of the selected variable
B. Sample Properties, Sample Statistics, sample data for the selected variable, and a histogram of the data for the selected variable
C. Sample Properties, Sample Statistics, sample data with all variables and values, and a histogram of the data for the selected variable
D. Sample Properties, Sample Statistics for all variables, and sample data for all variables

Answer: B

NEW QUESTION # 63
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 data. 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 median of the predicted probabilities of TARGET=1 in the scoring data is in which of the following ranges?
Response:

A. 0.50-0.849999
B. less than 0.149999
C. 0.15-0.499999
D. 0.85 or more

Answer: B

NEW QUESTION # 64
......

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