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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:

1. 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) 67409.93
B) 14.69
C) 5.00
D) 5862.76

2. 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) right skewed
B) normal
C) bimodal
D) left skewed

3. Which statement describes the Decision Tree Split Search mechanism for categorical inputs?
Select one:
Response:

A) The levels that have target rate of 0 or 100% are re-binned first, then weighted and the weights are used for testing.
B) All levels are weighted and the weights are used for testing.
C) The average target value is calculated for each level, and then passed on for testing if it is the optimal split point.
D) A clustering mechanism eliminates observations in outlier clusters as potential split points as a first step. Then, for the remaining observations, the average target value is calculated for each level, and then passed on for testing if it is the optimal split point.

4. Which of the following is not a good reason to"regularize" input distributions using a simple transformation?
Response:

A) Regression models are sensitive to extreme or outlying values in the input space.
B) One benefit is improved model performance.
C) Another benefit is ease in model interpretation.
D) When you perform regression, inputs with highly skewed or highly kurtotic distributions can be selected over inputs that would yield better overall predictions.

5. How many unique indicator variables were created during imputation?
Response:

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

Solutions: