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| Section | Objectives |
|---|---|
| Topic 1: Model Deployment | - Scoring and deployment workflows - Operationalizing predictive models |
| Topic 2: Data Preparation | - Feature selection and preprocessing - Data cleaning and transformation |
| Topic 3: Modeling Techniques | - Classification models - Regression and clustering methods |
| Topic 4: Model Evaluation and Validation | - Performance metrics - Model comparison and selection |
| Topic 5: Data Mining Fundamentals | - Business understanding and analytics objectives - Core concepts of data mining |
| Topic 6: CRISP-DM Methodology | - Application in SPSS Modeler workflows - Phases of CRISP-DM |
| Topic 7: IBM SPSS Modeler Environment | - Interface and stream design - Data flow and node usage |
1. Which of the following types of nodes will have data flowing both in and out, when used in a stream?
A) Sources
B) Graphs
C) Record Ops
D) Export
2. Which node can be used to easily generate a field that divides the data into subsamples for the Training and Testing stages of modeling?
A) Balance
B) Filler
C) Partition
D) Sample
3. Examine the Data Audit output shown below.
What action will occur in a generated Outlier & Extreme Supernode for HOLCOST?
A) Ten records will be set to the system missing (null) value for HOLCOST.
B) Ten records will be deleted.
C) Nine records will be set to the system missing (null) value for HOLCOST.
D) Nine records will be deleted.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: B |
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