Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Random Forest Regularizado× | Árbol de decisión regularizado× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2012 | 1984 |
| Autor original≠ | Deng, H. & Runger, G. | Breiman, L., Friedman, J., Olshen, R., & Stone, C. |
| Tipo≠ | Regularized ensemble (penalized feature selection in trees) | Supervised learning (regularized tree) |
| Fuente seminal≠ | Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8. DOI ↗ | Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8 |
| Alias | RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble | pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART |
| Relacionados≠ | 5 | 6 |
| Resumen≠ | Regularized Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy. | A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees. |
| ScholarGateConjunto de datos ↗ |
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