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| Ensemble Gradiens Erősítés× | Döntési fa× | LightGBM× | |
|---|---|---|---|
| Tudományterület | Gépi tanulás | Gépi tanulás | Gépi tanulás |
| Módszercsalád | Machine learning | Machine learning | Machine learning |
| Keletkezés éve≠ | 2001 | 1984 | 2017 |
| Megalkotó≠ | Friedman, J. H. | Breiman, Friedman, Olshen & Stone | Ke, G. et al. (Microsoft) |
| Típus≠ | Ensemble (sequential boosting of decision trees) | Recursive partitioning (if-then rules) | Gradient boosting decision tree ensemble |
| Alapmű≠ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | 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 ↗ |
| Alternatív nevek≠ | Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Kapcsolódó≠ | 6 | 5 | 5 |
| Összefoglaló≠ | Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data. | 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. |
| ScholarGateAdatkészlet ↗ |
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