Learn to go beyond the basic decision tree algorithms in KNIME by accessing WEKA, R, and Python-based decision tree and rule induction algorithms from within the KNIME platform.
Machine Learning and AI Foundations: Advanced Decision Trees with KNIME
Overview
Syllabus
Introduction
- Advanced decision trees
- What you should know
- Using the exercise files
- Why are trees considered greedy algorithms?
- Why are there so many algorithms?
- Five low node or no code options in KNIME
- Installing extensions
- WEKA LMT demonstration
- Interpreting the LMT results
- Comparing trees and rule induction
- Rule induction demo
- Interpreting the rules
- Low code options in KNIME
- Python script node demo
- CHAID demo in KNIME
- Advanced code options in KNIME (optimal sparse trees)
- Introducing random forest
- Random forests demo
- Comparing two models
- Data reduction with random forests
- The XAI view node
- Deployment
- Final thoughts and recommendations
Taught by
Keith McCormick
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