Comparar métodos

	SVM Explicável	Gradient Boosting Explicável
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2016–2017 (XAI layer)	2017–2020
Autor original	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)
Tipo	Post-hoc explainability applied to SVM	Ensemble + explainability layer
Fonte seminal	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.
Outros nomes	Explainable SVM, Interpretable SVM, XAI-SVM, Transparent Support Vector Machine	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
Relacionados	4	6
Resumo	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.