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Overview
"Machine Learning and AI: Advanced Decision Trees with SPSS" is a specialized course designed for data professionals and analysts seeking to deepen their expertise in predictive modeling using IBM SPSS. This course focuses on advanced decision tree techniques, going beyond the basics to explore powerful modeling methods and practical business applications. Participants will work with sophisticated decision tree algorithms such as CHAID, C&RT, and QUEST, learning how to choose the right model for specific data scenarios. The course covers model refinement,...
Syllabus
Introduction
- Welcome
- What you should know
- Using the exercise files
1. Understanding QUEST
- Overview
- How QUEST handles nominal variables
- How QUEST handles ordinal and continuous variables
- How QUEST handles missing data
- Pruning in QUEST
- Stopping rules in QUEST
2. Understanding C5.0
- ID3 and C4.5
- Winnowing attributes
- Rule sets
- Understanding information gain
- Pruning in C5.0
- How C5.0 handles missing data
3. Advanced Topics
- Ensembles
- What is bagging?
- Using bagging for feature selection
- Random forests
- What is boosting?
- Costs and priors
Conclusion
- Next steps
Instructor
Keith McCormick
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