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

1. For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:

A) It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.
B) It looks for a set of colinear inputs that correlate with the target.
C) It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
D) It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.

2. Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

A) The model has too few input variables.
B) The misclassification error for the test data is 0.154255.
C) All of the above
D) The optimization for the model has not been completed.

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

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

4. 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
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 average of the absolute values of parameter estimates between the input and all of the hidden neurons

5. If the bank wanted to select the best model based on which model achieves the highest lift for the third decile in the validation data, then which of the following is the best model?
Response:

A) Decision Tree
B) Neural Network
C) Decision Tree (3-way)
D) Regression

Solutions: