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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q38-Q43):

NEW QUESTION # 38
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 distribution of the predicted probabilities of TARGET=0 in the scoring data is approximately which of the following?
Response:

A. normal
B. left skewed
C. bimodal
D. right skewed

Answer: B

NEW QUESTION # 39
Refer to the exhibit:

What would be the decision threshold (probability cutoff) generated for this decision matrix. You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A. 0.27
B. 0.21
C. 0.18
D. 0.82

Answer: C

NEW QUESTION # 40
How many hidden layers are generally needed in an MLP-based neural network to capture a discontinuous relationship between inputs and target?
Response:

A. one hidden layer
B. three or more hidden layers
C. no hidden layer, direct connection between inputs and output is preferred
D. two hidden layers

Answer: D

NEW QUESTION # 41
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 average of the absolute values of parameter estimates between the input and all of the hidden neurons
B. 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
C. none of the above
D. the highest absolute value of the parameter estimate between the input and any of the hidden neurons

Answer: C

NEW QUESTION # 42
How many unique indicator variables were created during imputation?
Response:

A. none
B. 1-10
C. 11-20
D. 21 or higher

Answer: C

NEW QUESTION # 43
......

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