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| Section | Weight | Objectives |
|---|---|---|
| Data Understanding | 15% | - Explore and visualize data - Identify data quality issues |
| Deployment | 10% | - Reporting and documentation - Scoring new data - Stream export and integration |
| SPSS Modeler Professional Functionality | 10% | - Stream creation and management - Node palettes and SuperNodes - Scripting and automation |
| Business Understanding and Planning | 10% | - Align business objectives to data mining goals - CRISP-DM methodology |
| Data Preparation | 20% | - Sampling, partitioning and balancing - Filtering, aggregation and refinement - Data integration, merging and appending - Data transformation and cleaning |
| Modeling | 20% | - Auto-modeling and ensemble techniques - Clustering and segmentation models - Association and sequence models - Classification and regression models |
| Evaluation and Analysis | 15% | - Validation methods - Model performance assessment - Interpretation and visualization of results |
1. In the Gains Chart displayed below, what does the $BEST-CHURNED line represent?
A) The best current model to predict the field CHURNED
B) The best model for cases in the training partition to predict the field CHURNED
C) The theoretically best model to predict the field CHUREND
D) None of the above
2. What are the advantages of pruning Decision Tree models? (Select all that apply.)
A) Pruning in make the tree easier to interpret.
B) Pruning can increase the accuracy of the tree.
C) Pruning can help simplify the tree.
D) Pruning can improve the generalization of the tree.
3. Which statement accurately describes the function of the Coerce data check setting Type node?
A) Checks fully instantiated fields for values outside the specified range and reports unspecified values.
B) None of the above is accurate.
C) Checks all fields for values outside the specified range and reports unspecified values.
D) Checks fully instantiated fields for values outside the specified range and converts unspecified values to legal values.
4. Business objectives are the origin of every data mining solution.
A) True
B) False
5. In the Logistic node, can a Multinomial Model option be used when the target is flag field?
A) True
B) False
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C,D | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: A |
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