विधियों की तुलना करें

	सक्रिय अधिगम	लाइटजीबीएम	रैंडम फ़ॉरेस्ट
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	2009	2017	2001
प्रवर्तक	Burr Settles	Ke, G. et al. (Microsoft)	Breiman, L.
प्रकार	Interactive supervised learning framework	Gradient boosting decision tree ensemble	Ensemble (bagging of decision trees)
मौलिक स्रोत	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	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.
उपनाम	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
संबंधित	2	5	4
सारांश	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.	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.