Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Selitettävä Random Forest× | Päätöspuu× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2001–2017 | 1984 |
| Kehittäjä≠ | Breiman, L. (RF); Lundberg & Lee (SHAP attribution) | Breiman, Friedman, Olshen & Stone |
| Tyyppi≠ | Interpretable ensemble (bagging + post-hoc attribution) | Recursive partitioning (if-then rules) |
| Alkuperäislähde≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Rinnakkaisnimet≠ | XRF, interpretable random forest, transparent random forest, random forest with explainability | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike. | 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. |
| ScholarGateAineisto ↗ |
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