Outline

• Intro (5 minutes)
• What are random forests, how are they used, and what Python software is available for using them?
• What strengths do they have relative to other models (scalability and applicability to a broad range of problems)?
• Forest Internals (15 minutes)
• Decision Trees (5 minutes)
  • Presentation of the decision tree model, the building block of random forests.
• Entropy Minimization (5 minutes)
  • Explanation of how decision trees are tuned using entropy minimization.
• Building Forests from Decision Trees (5 minutes)
  • Explanation of how decision trees are aggregated to form random forests.
• Illustrative Examples (10 minutes)
• Regression on non-linear functions (5 minutes)
• Classification with unscaled features (5 minutes)