Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Полуавтоматическое дерево решений× | Градиентный бустинг× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2000s | 2001 |
| Автор метода≠ | Various (Levin & Shapiro; Zhu & Goldberg lineage) | Friedman, J. H. |
| Тип≠ | Semi-supervised classifier / regressor | Ensemble (sequential boosting of decision trees) |
| Основополагающий источник≠ | Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning. link ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Другие названия | SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Связанные≠ | 4 | 5 |
| Сводка≠ | A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming. | 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. |
| ScholarGateНабор данных ↗ |
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