Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Forklarbar LightGBM× | Beslutningstræ× | Gradient Boosting× | |
|---|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning | Machine learning |
| Oprindelsesår≠ | 2017 | 1984 | 2001 |
| Ophavsperson≠ | Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models) | Breiman, Friedman, Olshen & Stone | Friedman, J. H. |
| Type≠ | Gradient boosting with post-hoc explainability (SHAP) | Recursive partitioning (if-then rules) | Ensemble (sequential boosting of decision trees) |
| Oprindelig kilde≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Aliasser≠ | XAI-LightGBM, LightGBM with SHAP, Interpretable LightGBM, LightGBM explainability | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Relaterede≠ | 6 | 5 | 5 |
| Resumé≠ | 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. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
| ScholarGateDatasæt ↗ |
|
|
|