Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Robusti päätöspuu× | Päätöspuu× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2000s–2019 | 1984 |
| Kehittäjä≠ | Various (Chen & Nan 2019; robust statistics community) | Breiman, Friedman, Olshen & Stone |
| Tyyppi≠ | Supervised classification / regression tree | Recursive partitioning (if-then rules) |
| Alkuperäislähde≠ | Chen, H., & Nan, F. (2019). Robust Decision Trees Against Adversarial Examples. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1006–1015. link ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Rinnakkaisnimet≠ | robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | A Robust Decision Tree is a decision tree variant trained with modified splitting criteria or training procedures designed to reduce sensitivity to outliers, label noise, and adversarial perturbations. Rather than minimizing standard impurity measures that are strongly affected by extreme values, robust variants use statistically robust analogues or regularization to produce splits that generalize under noisy or corrupted data conditions. | 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. |
| ScholarGateAineisto ↗ |
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