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

1. 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.
Now suppose that the bank expects to make a profit of $200 USD when TARGET=1, but it expects to lose $25 USD when TARGET=0. Incorporate the above scenario, change the assessment measure of the decision tree to average square error, and then run the Decision Tree node. What is the total profit for the test data set?
Response:

A) 300-999
B) 1,600 or higher
C) 1,000-1,599
D) less than or equal to 299

2. Refer to the exhibit:

When the Explore button is selected in the graphic, what information will be displayed?
Select one:
Response:

A) Sample Properties, Sample Statistics, sample data with all variables and values, and a histogram of the data for the selected variable
B) Sample Properties, Sample Statistics, sample data for the selected variable, and a histogram of the data for the selected variable
C) Sample Properties, Sample Statistics for all variables, and sample data for all variables
D) Sample Properties, Sample Statistics, sample data for the selected variable, and a pie chart of the selected variable

3. What is the purpose of the Kass (Bonferroni) adjustment in the decision tree split-search algorithm?
Select one:
Response:

A) To give categorical inputs a greater chance to be used the split.
B) To ensure that the choice of split is not influenced by input measurement scale.
C) To ensure a non-negative logworth value.
D) To reduce the number of surrogate splitting rules.

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

A) All levels are weighted and the weights are used for testing.
B) The levels that have target rate of 0 or 100% are re-binned first, then 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.

5. Assume you have two equally appealing logistic regression models. Then, if you have to select only one out of these two models, you should select the one that has which of the following?
Response:

A) all of the above
B) smaller value of gamma
C) smaller value of SBC (Schwarz,s Bayesian criterion)
D) higher value of AIC (Akaike,s information criterion)

Solutions: