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| Гаусов смесен модел× | SHAP (SHapley Additive exPlanations)× | |
|---|---|---|
| Област | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 1977 | 2017 |
| Създател≠ | Dempster, Laird & Rubin (EM algorithm) | Lundberg, S.M. & Lee, S.-I. |
| Тип≠ | Probabilistic (soft) clustering — mixture model | Model-explanation method (Shapley-value attribution) |
| Основополагащ източник≠ | Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22. DOI ↗ | Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777. link ↗ |
| Други названия≠ | Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians | SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability |
| Свързани≠ | 4 | 5 |
| Резюме≠ | A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation. | 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). |
| ScholarGateНабор от данни ↗ |
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