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| Erklärbares LightGBM× | SHAP (SHapley Additive exPlanations)× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr | 2017 | 2017 |
| Urheber≠ | Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models) | Lundberg, S.M. & Lee, S.-I. |
| Typ≠ | Gradient boosting with post-hoc explainability (SHAP) | Model-explanation method (Shapley-value attribution) |
| Wegweisende Quelle≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. 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 ↗ |
| Aliasnamen | XAI-LightGBM, LightGBM with SHAP, Interpretable LightGBM, LightGBM explainability | SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Explainable LightGBM combines Microsoft's LightGBM gradient boosting framework with SHAP (SHapley Additive exPlanations) to deliver both high predictive performance and rigorous, theoretically grounded feature-level explanations. It is widely adopted in applied research where predictive accuracy and interpretability are simultaneously required. | 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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