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| 설명 가능한 엑스트라 트리× | 엑스트라 트리 (Extra Trees)× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2006 (Extra Trees); 2017 (SHAP integration) | 2006 |
| 창시자≠ | Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer) | Geurts, P.; Ernst, D.; Wehenkel, L. |
| 유형≠ | Ensemble (randomized trees) with post-hoc explainability | Ensemble (extremely randomized decision trees) |
| 원전≠ | Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| 별칭 | XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| 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. | Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time. |
| ScholarGate데이터셋 ↗ |
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