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| Selitettävä äänestysyhtye (Explainable Voting Ensemble)× | Selitettävä gradienttitehostus× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2016–2020 | 2017–2020 |
| Kehittäjä≠ | Composite: voting ensemble (Dietterich, 2000) + XAI frameworks (Ribeiro et al., 2016; Lundberg & Lee, 2017) | Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles) |
| Tyyppi≠ | Ensemble with post-hoc or ante-hoc interpretability | Ensemble + explainability layer |
| 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 ↗ | Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67. DOI ↗ |
| Rinnakkaisnimet | XAI voting ensemble, interpretable voting classifier, transparent voting ensemble, explainable majority vote model | XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | An Explainable Voting Ensemble combines predictions from multiple diverse base models through majority vote (hard voting) or averaged probabilities (soft voting), then applies post-hoc or ante-hoc XAI techniques — such as SHAP values, LIME, or permutation importance — to produce feature-level explanations for the combined model's decisions. The goal is to retain the accuracy gains of ensemble aggregation while meeting interpretability requirements in high-stakes or regulated applications. | Explainable Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics. |
| ScholarGateAineisto ↗ |
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