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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 after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
Which of the following variables was used in the decision tree model?
Response:

A) TLDel3060Cnt24
B) TLDel90Cnt24
C) IMP_TLSatCnt
D) InqFinanceCnt24.

2. Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
Consider the variable TLCnt03 in the selected model. Based on the model results, changing this variable by 1 unit will result in which of the following?
Response:

A) reduction of odds for TARGET=1 by 0.3457
B) change of odds for TARGET=1 by a factor 0.708
C) change of odds for TARGET=1 by a factor 0.3457
D) reduction of odds for TARGET=1 by 0.708

3. What percentage of observations in the test data has TARGET=1?
Response:

A) 16.6627
B) 83.3333
C) 16.8874
D) 16.5924

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

5. Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

A) decrease the performance of logistic regression
B) create convergence problems in maximum likelihood estimation
C) increase the performance of logistic regression
D) neither increase nor decrease the performance of logistic regression

Solutions: