A00-255 RELEVANT ANSWERS | A00-255 BRAIN DUMP FREE

A00-255 Relevant Answers | A00-255 Brain Dump Free

A00-255 Relevant Answers | A00-255 Brain Dump Free

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q84-Q89):

NEW QUESTION # 84
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 percentage of all observations is being correctly predicted in the test data set by the decision tree?
Response:

  • A. 85.2222%
  • B. 83.1126%
  • C. 84.5212%
  • D. 16.8874%

Answer: B


NEW QUESTION # 85
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:

  • A. 84-89.99%
  • B. 90-93.99%-
  • C. less than 83.99%
  • D. 94% or higher

Answer: C


NEW QUESTION # 86
Sometimes in predictive modeling we build models using a sample that has a primary outcome proportion different from true population proportion. This is usually done when the ratio of primary to secondary outcomes in a binary target variable in the population is close to which of the following?
Response:

  • A. 1.2
  • B. 0.05
  • C. 0
  • D. 0.8

Answer: B


NEW QUESTION # 87
For an ordinal variable with L levels, the number of possible splits in a decision tree is which of the following?
Response:

  • A. 2L-1-1
  • B. 2L
  • C. 2L-1
  • D. (L-1)/1

Answer: A


NEW QUESTION # 88
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. 7% or higher
  • B. under 4.99%
  • C. 6%-6.99%
  • D. 5%-5.99%

Answer: B


NEW QUESTION # 89
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