方法对比

	t-SNE	SHAP (SHapley Additive exPlanations)
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2008	2017
提出者	van der Maaten, L. & Hinton, G.	Lundberg, S.M. & Lee, S.-I.
类型	Nonlinear dimensionality reduction (manifold visualization)	Model-explanation method (Shapley-value attribution)
开创性文献	van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.	Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777.
别名	t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne	SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability
相关	3	5
摘要	t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods.	SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did).