3. Decision Trees

Syllabus Points Covered

Software automation

Algorithms in machine learning

• Explore models of training ML

  • semi-supervised learning

• Investigate common applications of key ML algorithms

  • data analysis and forecasting

• Research models used by software engineers to design and analyse ML

  • decision trees

Chapter Contents

• 3.1. Decision Trees
  • 3.1.1. Glossary
• 3.2. Building a Classification Tree
• 3.3. Classifying With a Classification Tree
• 3.4. Node Impurity and Tree Height
• 3.5. A Semi-Supervised Model
  • 3.5.1. Glossary
• 3.6. Random Forests
  • 3.6.1. Glossary
• 3.7. Extension: Building a Classification Tree
• 3.8. Extension: Interpreting The Output Graph
• 3.9. Extension: Predicting With a Classification Tree
• 3.10. Building a Regression Tree
• 3.11. Predicting With a Regression Tree
• 3.12. Extension: Building and Predicting With A Regression Tree
• 3.13. Semi-Supervised Learning and Random Forests
  • 3.13.1. Random Forests
  • 3.13.2. Semi-Supervised Learning
• 3.14. Interpreting Decision Trees