Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| SHAP (SHapley Additive exPlanations)× | Beslisboom× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2017 | 1984 |
| Grondlegger≠ | Lundberg, S.M. & Lee, S.-I. | Breiman, Friedman, Olshen & Stone |
| Type≠ | Model-explanation method (Shapley-value attribution) | Recursive partitioning (if-then rules) |
| Oorspronkelijke bron≠ | Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777. link ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Aliassen≠ | SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Verwant | 5 | 5 |
| Samenvatting≠ | 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). | 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. |
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