Free PDF 2025 SASInstitute Accurate A00-255: Advanced SAS Predictive Modeling Using SAS Enterprise Miner 14 Testing Engine
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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q53-Q58):
NEW QUESTION # 53
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:
Answer: A
NEW QUESTION # 54
What percentage of observations in the test data has TARGET=1?
Response:
Answer: C
NEW QUESTION # 55
Which of the following sequential selection methods do you use so that SAS Enterprise Miner will look at all variables already included in the model and delete any variable that is not significant at the specified level?
Response:
Answer: A
NEW QUESTION # 56
What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:
Answer: A
NEW QUESTION # 57
Consider a binary target variable. Assume Accuracy is the desired assessment measure. Accuracy is not an option in the Decision Tree node. Which assessment measure can you use as a proxy for accuracy?
Select one:
Response:
Answer: C
NEW QUESTION # 58
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