Classical Machine Learning / tree-models

Tree-based models

Decision trees, entropy, Gini, random forests, bagging, boosting, XGBoost, LightGBM, and CatBoost.

shellbackend needed later
Focus
  • split criteria
  • bagging vs boosting
  • feature importance
  • categorical handling
Future feature idea

Decision tree, random forest, and boosting comparison with feature importance.

Self-check
  1. What is the core job of "Tree-based models"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.