השוואת שיטות

	Active Learning LightGBM	למידה פעילה	יער אקראי
תחום	למידת מכונה	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning	Machine learning
שנת המקור	2017–present	2009	2001
הוגה השיטה	Settles, B. (active learning); Ke, G. et al. (LightGBM)	Burr Settles	Breiman, L.
סוג	Hybrid (active learning query strategy + gradient boosting classifier)	Interactive supervised learning framework	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.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
כינויים	AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
קשורות	5	2	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.	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.	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.