rf-struggles-multiple-categorical-variables

IN premise — entries/2026/06/21/wiki-Random_forest-chunk-4.md

Created 2026-06-21T09:55:52+00:00

Random forests underperform on problems involving multiple categorical variables.

Dependents

These beliefs depend on this one:

• OUT classical-ml-sufficient-for-structured-problems — Classical ML methods would be sufficient for most structured data problems — SVMs provide a rare theoretically complete framework with convex optimization and global guarantees, while random forests achieve robust generalization through variance reduction and built-in OOB validation without risk of overfitting from added trees.
• OUT rf-universally-applicable-ensemble — Random forests are universally applicable ensemble methods — no overfitting from added trees, built-in OOB validation, and robust variance reduction make them a safe default for any problem.