Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Explainable Extra Trees× | Uimarishaji wa Mteremko× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2006 (Extra Trees); 2017 (SHAP integration) | 2001 |
| Mwanzilishi≠ | Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer) | Friedman, J. H. |
| Aina≠ | Ensemble (randomized trees) with post-hoc explainability | Ensemble (sequential boosting of decision trees) |
| Chanzo asilia≠ | Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Majina mbadala | XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | Explainable Extra Trees combines the Extremely Randomized Trees (Extra Trees) ensemble algorithm with post-hoc explainability methods — most commonly SHAP values — to deliver both strong predictive performance and transparent, feature-level explanations. It extends the classic Extra Trees classifier or regressor so that every prediction can be decomposed into individual feature contributions, satisfying demands for accountability in applied and regulated domains. | 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. |
| ScholarGateSeti ya data ↗ |
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