Võrdle meetodeid

	LightGBM	Juhuslik mets	XGBoost
Valdkond	Masinõpe	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	2017	2001	2016
Looja	Ke, G. et al. (Microsoft)	Breiman, L.	Chen, T. & Guestrin, C.
Tüüp	Gradient boosting decision tree ensemble	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted decision trees)
Algallikas	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Rööpnimetused	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Seotud	5	4	5
Kokkuvõte	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.