Machine Learning and AI Foundations: Advanced Decision Trees with KNIME

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.

Introduction

• Advanced decision trees
• What you should know
• Using the exercise files

1. Exploring the Many Decision Tree Algorithms

• Why are trees considered greedy algorithms?
• Why are there so many algorithms?
• Five low node or no code options in KNIME

2. Using Extensions

• Installing extensions
• WEKA LMT demonstration
• Interpreting the LMT results

3. What Is Rule Induction?

• Comparing trees and rule induction
• Rule induction demo
• Interpreting the rules

4. Low Code Python Options in KNIME

• Low code options in KNIME
• Python script node demo
• CHAID demo in KNIME
• Advanced code options in KNIME (optimal sparse trees)

5. Ensembles and Random Forests

• Introducing random forest
• Random forests demo
• Comparing two models

6. Advanced Tips and Tricks

• Data reduction with random forests
• The XAI view node
• Deployment

Conclusion

• Final thoughts and recommendations