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NEW QUESTION # 35
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:
Answer: C
NEW QUESTION # 36
Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:
Answer: D
NEW QUESTION # 37
What percentage of observations in the test data has TARGET=1?
Response:
Answer: B
NEW QUESTION # 38
For the variable InqCnt06, replace all values over 10.1 with the value 10. How many values are replaced?
Response:
Answer: A
NEW QUESTION # 39
Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
The percentage of observations correctly predicted in the validation data by the Ensemble model is in which of the following ranges?
Response:
Answer: B
NEW QUESTION # 40
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