Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Árbol de Decisión Robusto× | Random Forest× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2000s–2019 | 2001 |
| Autor original≠ | Various (Chen & Nan 2019; robust statistics community) | Breiman, L. |
| Tipo≠ | Supervised classification / regression tree | Ensemble (bagging of decision trees) |
| Fuente seminal≠ | 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. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 6 | 4 |
| Resumen≠ | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateConjunto de datos ↗ |
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