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| 説明可能なランダムフォレスト× | 勾配ブースティング× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2001–2017 | 2001 |
| 提唱者≠ | Breiman, L. (RF); Lundberg & Lee (SHAP attribution) | Friedman, J. H. |
| 種類≠ | Interpretable ensemble (bagging + post-hoc attribution) | Ensemble (sequential boosting of decision trees) |
| 原典≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 別名 | XRF, interpretable random forest, transparent random forest, random forest with explainability | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| 関連≠ | 4 | 5 |
| 概要≠ | Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike. | 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. |
| ScholarGateデータセット ↗ |
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