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| Објашњиви ЛајтГБМ× | CatBoost× | |
|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 2017 | 2018 |
| Tvorac≠ | Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models) | Prokhorenkova, L. et al. (Yandex) |
| Tip≠ | Gradient boosting with post-hoc explainability (SHAP) | Gradient boosting on decision trees |
| Temeljni izvor≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ |
| Drugi nazivi | XAI-LightGBM, LightGBM with SHAP, Interpretable LightGBM, LightGBM explainability | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma |
| Srodne≠ | 6 | 5 |
| Sažetak≠ | 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. | CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data. |
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