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| 설명 가능한 엑스트라 트리× | 결정 트리× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2006 (Extra Trees); 2017 (SHAP integration) | 1984 |
| 창시자≠ | Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer) | Breiman, Friedman, Olshen & Stone |
| 유형≠ | Ensemble (randomized trees) with post-hoc explainability | Recursive partitioning (if-then rules) |
| 원전≠ | Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| 별칭≠ | XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| 관련 | 5 | 5 |
| 요약≠ | Explainable Extra Trees combines the Extremely Randomized Trees (Extra Trees) ensemble algorithm with post-hoc explainability methods — most commonly SHAP values — to deliver both strong predictive performance and transparent, feature-level explanations. It extends the classic Extra Trees classifier or regressor so that every prediction can be decomposed into individual feature contributions, satisfying demands for accountability in applied and regulated domains. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. |
| ScholarGate데이터셋 ↗ |
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