User Guide

• 1. Supervised Decision Trees
  • 1.1. Oblique Trees
    • 1.1.1. Differences compared to decision trees
    • 1.1.2. Mathematical formulation
    • 1.1.3. Classification, regression and multi-output problems
    • 1.1.4. Complexity
    • 1.1.5. Tips on practical use
    • 1.1.6. Limitations compared to decision trees
  • 1.2. Honest Trees
• 2. Unsupervised Decision Trees
  • 2.1. Unsupervised Criterion
    • 2.1.1. Two-Means
    • 2.1.2. Fast-BIC
  • 2.2. Evaluating Unsupervised Trees
• 3. Oblique Random Forests
• 4. Feature importance evaluation