Vertaile menetelmiä

	Aktiivisen oppimisen LightGBM	Gradient Boosting	LightGBM	Random Forest
Tieteenala	Koneoppiminen	Koneoppiminen	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning	Machine learning
Syntyvuosi	2017–present	2001	2017	2001
Kehittäjä	Settles, B. (active learning); Ke, G. et al. (LightGBM)	Friedman, J. H.	Ke, G. et al. (Microsoft)	Breiman, L.
Tyyppi	Hybrid (active learning query strategy + gradient boosting classifier)	Ensemble (sequential boosting of decision trees)	Gradient boosting decision tree ensemble	Ensemble (bagging of decision trees)
Alkuperäislähde	Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	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.
Rinnakkaisnimet	AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Liittyvät	5	5	5	4
Tiivistelmä	Active Learning LightGBM couples the query-efficient label-selection strategy of active learning with the speed and accuracy of LightGBM, a histogram-based gradient boosting framework. The model iteratively selects the most informative unlabeled instances for human annotation, retrains LightGBM on the growing labeled set, and converges to high accuracy with far fewer labeled examples than passive supervised learning.	Gradient 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.	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.