Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Árbol de Decisión× | Modelo Aditivo Generalizado (GAM)× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1984 | 1986 |
| Autor original≠ | Breiman, Friedman, Olshen & Stone | Trevor Hastie & Robert Tibshirani |
| Tipo≠ | Recursive partitioning (if-then rules) | Semi-parametric additive regression model |
| Fuente seminal≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ |
| Alias≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | 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. | A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response. |
| ScholarGateConjunto de datos ↗ |
|
|