विधियों की तुलना करें

	Explainable Support Vector Machine	व्याख्यायोग्य ग्रेडिएंट बूस्टिंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2016–2017 (XAI layer)	2017–2020
प्रवर्तक	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)
प्रकार	Post-hoc explainability applied to SVM	Ensemble + explainability layer
मौलिक स्रोत	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.
उपनाम	Explainable SVM, Interpretable SVM, XAI-SVM, Transparent Support Vector Machine	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
संबंधित	4	6
सारांश	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.