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Krahasoni metodat

Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.

Përmbledhja me Gradient (Gradient Boosting)×Pemët e vendimmarrjes×LightGBM×
FushaMësimi i makinësMësimi i makinësMësimi i makinës
FamiljaMachine learningMachine learningMachine learning
Viti i origjinës200119842017
KrijuesiFriedman, J. H.Breiman, Friedman, Olshen & StoneKe, G. et al. (Microsoft)
LlojiEnsemble (sequential boosting of decision trees)Recursive partitioning (if-then rules)Gradient boosting decision tree ensemble
Burimi themeluesFriedman, 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 ↗
Emërtime të tjeraGradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machineKarar Ağacı (Decision Tree), karar ağacı, classification tree, regression treeLightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
Të lidhura555
PërmbledhjaGradient 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.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.
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ScholarGateKrahasoni metodat: Gradient Boosting · Decision Tree · LightGBM. Marrë më 2026-06-19 nga https://scholargate.app/sq/compare