Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Random Forest× | Árbol de Decisión× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2001 | 1984 |
| Autor original≠ | Breiman, L. | Breiman, Friedman, Olshen & Stone |
| Tipo≠ | Ensemble (bagging of decision trees) | Recursive partitioning (if-then rules) |
| Fuente seminal≠ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Alias≠ | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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