four-interpretable-model-families

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

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

The four families of easily interpretable ML models are decision trees, linear models, rule-based models, and attention-based models.

Dependents

These beliefs depend on this one:

• IN interpretability-inversely-correlated-with-capability — There is a tension between interpretability and model complexity in ML: easily interpretable model families (decision trees, linear models, rule-based models, attention-based models) tend to be simpler, while neural networks that achieve strong performance are 'black box' models requiring separate XAI research to explain. Even within a single family, scaling from a single decision tree to a random forest ensemble trades interpretability for accuracy.