Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Дърво на решенията× | LightGBM× | Случайна гора× | |
|---|---|---|---|
| Област | Машинно обучение | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Година на възникване≠ | 1984 | 2017 | 2001 |
| Създател≠ | Breiman, Friedman, Olshen & Stone | Ke, G. et al. (Microsoft) | Breiman, L. |
| Тип≠ | Recursive partitioning (if-then rules) | Gradient boosting decision tree ensemble | Ensemble (bagging of decision trees) |
| Основополагащ източник≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Други названия≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Свързани≠ | 5 | 5 | 4 |
| Резюме≠ | 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. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateНабор от данни ↗ |
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