Võrdle meetodeid

	K-Nearest Neighbors	Logistiline regressioon
Valdkond	Masinõpe	Uurimisstatistika
Perekond	Machine learning	Process / pipeline
Tekkeaasta	1967	1958
Looja	Cover, T.M. & Hart, P.E.	David Roxbee Cox
Tüüp	Instance-based (non-parametric) learning	Method
Algallikas	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Rööpnimetused	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	logit model, binomial logistic regression, LR
Seotud	5	3
Kokkuvõte	K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.