Porovnat metody

	Bayesovské k-nejbližších sousedů	Naive Bayes	Random Forest
Obor	Strojové učení	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	2002	1997	2001
Tvůrce	Holmes, C. C. & Adams, N. M.	Mitchell, T. M. (textbook treatment)	Breiman, L.
Typ	Probabilistic instance-based classifier	Probabilistic classifier (Bayes' theorem with conditional independence)	Ensemble (bagging of decision trees)
Původní zdroj	Holmes, C. C., & Adams, N. M. (2002). A probabilistic nearest neighbour method for statistical pattern recognition. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2), 295–306.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Další názvy	Bayesian KNN, BKNN, probabilistic k-nearest neighbors, Bayesian nearest-neighbor classifier	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Příbuzné	3	4	4
Shrnutí	Bayesian k-Nearest Neighbors (Bayesian KNN) extends the classical KNN algorithm by placing a prior distribution over the neighborhood size k and combining likelihood evidence from neighbors with that prior to produce calibrated posterior class probabilities. It retains KNN's intuitive instance-based logic while adding principled uncertainty quantification over predictions.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.	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.