SAS PREDICTIVE MODELING USING SAS ENTERPRISE MINER 14 ACTUAL EXAM & A00-255 PRACTICE VCE & SAS PREDICTIVE MODELING USING SAS ENTERPRISE MINER 14 UPDATED TORRENT

SAS Predictive Modeling Using SAS Enterprise Miner 14 Actual Exam & A00-255 Practice Vce & SAS Predictive Modeling Using SAS Enterprise Miner 14 Updated Torrent

SAS Predictive Modeling Using SAS Enterprise Miner 14 Actual Exam & A00-255 Practice Vce & SAS Predictive Modeling Using SAS Enterprise Miner 14 Updated Torrent

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

NEW QUESTION # 59
Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:

1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Cubic Clustering Criterion statistic for this clustering?
Response:

  • A. 5862.76
  • B. 67409.93
  • C. 14.69
  • D. 5.00

Answer: C


NEW QUESTION # 60
Assume the Target has an event proportion of 2% in the original data. Which of the following property values should be used in the Sample node of SAS Enterprise Miner to create a sample from that data with a balanced 50/50 split for Target?
Select one:
Response:

  • A. Sample Method: Random and Criterion: Equal
  • B. Sample Method: Random and Criterion: Proportional
  • C. Sample Method: Stratify and Criterion: Proportional
  • D. Sample Method: Stratify and Criterion: Equal

Answer: D


NEW QUESTION # 61
The importance of an input variable in predicting a target in an MLP-based neural network can be figured out by which of the following?
Response:

  • A. the highest absolute value of the parameter estimate between the input and any of the hidden neurons multiplied by the absolute value of the parameter estimate of the hidden neuron
  • B. the highest absolute value of the parameter estimate between the input and any of the hidden neurons
  • C. the average of the absolute values of parameter estimates between the input and all of the hidden neurons
  • D. none of the above

Answer: D


NEW QUESTION # 62
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 # 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. 0.15-0.499999
  • B. 0.85 or more
  • C. 0.50-0.849999
  • D. less than 0.149999

Answer: D


NEW QUESTION # 64
......

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