Salīdzināt metodes

	Skaidrojams balsošanas ansamblis	Paskaidrojamā gradientu pastiprināšana
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2016–2020	2017–2020
Autors	Composite: voting ensemble (Dietterich, 2000) + XAI frameworks (Ribeiro et al., 2016; Lundberg & Lee, 2017)	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)
Tips	Ensemble with post-hoc or ante-hoc interpretability	Ensemble + explainability layer
Pirmavots	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	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.
Citi nosaukumi	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
Saistītās	6	6
Kopsavilkums	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.