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| 로버스트 랜덤 포레스트× | 결정 트리× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000s–2010s | 1984 |
| 창시자≠ | Various (extensions of Breiman 2001 Random Forest) | Breiman, Friedman, Olshen & Stone |
| 유형≠ | Robust Ensemble (noise-tolerant bagging of decision trees) | Recursive partitioning (if-then rules) |
| 원전≠ | Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18. link ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| 별칭≠ | RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| 관련≠ | 6 | 5 |
| 요약≠ | Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect. | 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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