Σύγκριση μεθόδων

	Επεξηγήσιμη Μηχανή Υποστήριξης Διανυσμάτων	Επεξηγήσιμο Gradient Boosting
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2016–2017 (XAI layer)	2017–2020
Δημιουργός	Cortes & Vapnik (SVM); explainability layer via Lundberg & Lee (SHAP, 2017) and Ribeiro et al. (LIME, 2016)	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)
Τύπος	Post-hoc explainability applied to SVM	Ensemble + explainability layer
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Explainable SVM, Interpretable SVM, XAI-SVM, Transparent Support Vector Machine	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
Συναφείς	4	6
Σύνοψη	Explainable SVM combines a trained Support Vector Machine with a post-hoc interpretability layer — typically SHAP or LIME — to produce feature-level explanations for individual predictions and global importance rankings. It retains the discriminative power of SVM while meeting transparency requirements in high-stakes domains such as medicine, finance, and law.	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.