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| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 20% | - Export score code - Model comparison in Model Studio - Register and publish models - Fit statistics, ROC, Lift and comparison reports - Model evaluation principles |
| Data Sources | 30% | - Reduce dimensionality and handle redundancy - Use Variable Selection node - Explore and modify data - Understand variable types and measurement scales - Create a project in Model Studio |
| Building Models | 50% | - Decision trees and ensemble models - Regression models - Support vector machines - Incorporate external and custom code - Neural networks - Autotuning and regularization - Supervised learning concepts |
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