方法对比

	主动学习线性回归	贝叶斯线性回归	随机森林
领域	机器学习	贝叶斯	机器学习
方法族	Machine learning	Bayesian methods	Machine learning
起源年份	1996	2013 (modern reference); foundations 18th–19th century	2001
提出者	Cohn, D. A.; Ghahramani, Z.; Jordan, M. I.	Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.	Breiman, L.
类型	Active learning / iterative supervised learning	Bayesian linear model	Ensemble (bagging of decision trees)
开创性文献	Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
别名	AL-LR, active linear regression, query-based linear regression, optimal experimental design for regression	bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
相关	2	4	4
摘要	Active Learning Linear Regression is an iterative machine-learning approach that couples a linear regression model with an intelligent query strategy to select the most informative unlabeled points for labeling. By focusing labeling effort where uncertainty is highest, it achieves competitive predictive accuracy with far fewer labeled examples than passive random sampling.	Bayesian linear regression is a probabilistic extension of the ordinary linear model, introduced through Bayes' rule and formalised in its modern computational workflow by Gelman et al. (2013). Rather than returning a single point estimate for each coefficient, it combines a user-specified prior distribution with the likelihood of the observed data to produce a full posterior distribution over all parameters, from which credible intervals and posterior predictive distributions are derived.	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.