השוואת שיטות

	K-שכנים קרובים בייסיאני	בייס נאיבי
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2002	1997
הוגה השיטה	Holmes, C. C. & Adams, N. M.	Mitchell, T. M. (textbook treatment)
סוג	Probabilistic instance-based classifier	Probabilistic classifier (Bayes' theorem with conditional independence)
מקור מכונן	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
כינויים	Bayesian KNN, BKNN, probabilistic k-nearest neighbors, Bayesian nearest-neighbor classifier	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
קשורות	3	4
תקציר	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.