השוואת שיטות

	Active Learning LightGBM	LightGBM	יער אקראי
תחום	למידת מכונה	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning	Machine learning
שנת המקור	2017–present	2017	2001
הוגה השיטה	Settles, B. (active learning); Ke, G. et al. (LightGBM)	Ke, G. et al. (Microsoft)	Breiman, L.
סוג	Hybrid (active learning query strategy + gradient boosting classifier)	Gradient boosting decision tree ensemble	Ensemble (bagging of decision trees)
מקור מכונן	Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.	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.
כינויים	AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
קשורות	5	5	4
תקציר	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.	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.