Method evidence record
Explainable Decision Tree
An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
Explainable Decision Tree (Interpretable Rule-Based Classification and Regression Tree)
Taxonomic method record · ml-model / machine-learning
- Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. · ISBN 978-0-412-04841-8
- Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. · DOI 10.1038/s42256-019-0048-x
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
No curated claims yet
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.