مقایسهٔ روش‌ها

	ماشین بردار پشتیبانِ قابل‌تفسیر	تشریح تقویت گرادیان (Explainable Gradient Boosting)
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2016–2017 (XAI layer)	2017–2020
پدیدآور	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)
نوع	Post-hoc explainability applied to SVM	Ensemble + explainability layer
منبع بنیادین	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.
نام‌های دیگر	Explainable SVM, Interpretable SVM, XAI-SVM, Transparent Support Vector Machine	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
مرتبط	4	6
خلاصه	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.