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
Related Courses
-
Machine Learning: Random Forests & Decision Trees
-
Machine Learning with Python: Zero to GBMs
-
Decision Trees, Random Forests, AdaBoost & XGBoost in Python
-
Machine Learning with Tree-Based Models in R
-
Introduction to Machine Learning in Sports Analytics
-
Build Decision Trees, SVMs, and Artificial Neural Networks
