Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Vysvětlitelné K-nejbližších sousedů× | Random Forest× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1967 (KNN); 2010s (explainability extensions) | 2001 |
| Tvůrce≠ | Cover, T. & Hart, P. (KNN); XAI extensions by various authors | Breiman, L. |
| Typ≠ | Instance-based learning with explainability layer | Ensemble (bagging of decision trees) |
| Původní zdroj≠ | Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Další názvy | XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Příbuzné | 4 | 4 |
| Shrnutí≠ | Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers. | 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. |
| ScholarGateDatová sada ↗ |
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