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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Scoring and Deployment | 20% | - Model deployment - Model scoring |
| Topic 2: Building Predictive Models | 40% | - Decision trees - Neural networks - Regression models - Gradient boosting |
| Topic 3: Data Preparation and Exploration | 20% | - Data quality - Data exploration using SAS Visual Statistics - Importing data |
| Topic 4: Model Assessment and Selection | 20% | - Model fit statistics - Model comparison - ROC and Lift curves |
1. What are some features of model comparison in SAS Visual Statistics?
A) Evaluating model stability over time
B) Identifying the best model based on a single metric
C) Assessing the impact of predictor variables on model outcomes
D) Comparing the performance of different models
2. You would like to see the minimum and maximum values for all of your measures so that you can filter variables as needed.
Which is the most efficient way to do that?
A) Create a calculated item subtracting the Min aggregation from the Max aggregation.
B) Select View Measure Details within the Actions menu to the right of the dataset name.
C) Select a histogram object for each measure.
D) Create aggregated measures using the Min and Max aggregations.
3. Refer to the exhibit:
How many predictor variables would be included in the score code produced by this model?
A) 13
B) 8
C) 5
D) 7
4. Refer to the fit summary from in the exhibit below.
What can be concluded from the fit summary?
A) Job Type = Office has no important variables associated with it.
B) Debt to income ratio is a significant predictor when Job Type = Mgr.
C) Debt to income ratio is a significant predictor when Job Type = Sales.
D) Job Type is not a significant predictor in this model.
5. Refer to the fit summary from SAS Visual Statisitcs in the exhibit below.
What can be concluded from the fit summary?
A) Average Sales is an important predictor when Customer Value Level = C.
B) Customer Value Level C has no important variables associated with it.
C) Customer Value Level is not a significant predictor in this model.
D) Average Sales is a significant predictor when Customer Value Level = E.
Solutions:
| Question # 1 Answer: B,D | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: D |
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