Work toward a mastery of machine learning by exploring advanced decision tree algorithm concepts. Learn about the QUEST and C5.0 algorithms and a few advanced topics.
Machine Learning and AI: Advanced Decision Trees with SPSS
Overview
Syllabus
Introduction
- Advanced decision trees with SPSS
- What you should know
- Using the exercise files
- 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
- ID3 and C4.5
- Winnowing attributes
- Rule sets
- Understanding information gain
- Pruning in C5.0
- How C5.0 handles missing data
- Ensembles
- What is bagging?
- Using bagging for feature selection
- Random forests
- What is boosting?
- What is XGBoost?
- XGBoost Tree node
- Costs and priors
- XGBoost Linear
- Next steps
Taught by
Keith McCormick
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