Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Регуляризований випадковий ліс× | Дерево рішень× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2012 | 1984 |
| Автор методу≠ | Deng, H. & Runger, G. | Breiman, Friedman, Olshen & Stone |
| Тип≠ | Regularized ensemble (penalized feature selection in trees) | Recursive partitioning (if-then rules) |
| Основоположне джерело≠ | Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Інші назви≠ | RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Пов'язані | 5 | 5 |
| Підсумок≠ | Regularized Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy. | 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. |
| ScholarGateНабір даних ↗ |
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