Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| t-SNE× | SHAP (SHapley Additive exPlanations)× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2008 | 2017 |
| Tvůrce≠ | van der Maaten, L. & Hinton, G. | Lundberg, S.M. & Lee, S.-I. |
| Typ≠ | Nonlinear dimensionality reduction (manifold visualization) | Model-explanation method (Shapley-value attribution) |
| Původní zdroj≠ | van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605. link ↗ | Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777. link ↗ |
| Další názvy≠ | t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne | SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability |
| Příbuzné≠ | 3 | 5 |
| Shrnutí≠ | 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). |
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