Bandingkan kaedah

	Mesin Vektor Sokongan Boleh Dijelaskan	Pohon Keputusan Boleh Dijelas
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2016–2017 (XAI layer)	1984 (CART); XAI framing formalized 2010s–2020s
Pengasas	Cortes & Vapnik (SVM); explainability layer via Lundberg & Lee (SHAP, 2017) and Ribeiro et al. (LIME, 2016)	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.
Jenis	Post-hoc explainability applied to SVM	Interpretable supervised learning model
Sumber perintis	Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8
Alias	Explainable SVM, Interpretable SVM, XAI-SVM, Transparent Support Vector Machine	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree
Berkaitan	4	4
Ringkasan	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.	An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.