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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Predictive Model Assessment and Implementation | 25β30% | - Score and deploy models - Evaluate performance via profit/loss and comparison - Adjust for oversampling and sampling methods - Apply appropriate fit statistics |
| Topic 2: Pattern Analysis | 10β15% | - Identify clusters and segments - Interpret pattern discovery results |
| Topic 3: Data Sources | 20β25% | - Explore and assess data sources - Modify and prepare source data for modeling - Create data sources from SAS tables |
| Topic 4: Building Predictive Models | 35β40% | - Understand predictive modeling concepts - Build models using neural networks - Build models using decision trees - Build models using regression techniques |
What is the kurtosis value for the variable TLDel60Cnt24?
Response:
Correct Answer: C π³οΈ
What percentage of observations in the test data has TARGET=1?
Response:
Correct Answer: A π³οΈ
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.
Which of the following variable(s) is (are) statistically significant at the 5% level in the selected model?
Response:
Correct Answer: B π³οΈ
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.
Suppose that the data has been oversampled and the probability that TARGET=1 is 0.10 in the population. Incorporate the above scenario and run the Decision Tree node again.
What is the misclassification rate in the validation data set?
Response:
Correct Answer: A π³οΈ
What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:
Correct Answer: D π³οΈ
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