השוואת שיטות

	ניתוח דיסקרימיננטי ליניארי (LDA	שיטת השכנים הקרובים ביותר (K-Nearest Neighbors)	רגרסיה לוגיסטית
תחום	סטטיסטיקה	למידת מכונה	סטטיסטיקה למחקר
משפחה	Hypothesis test	Machine learning	Process / pipeline
שנת המקור	1936	1967	1958
הוגה השיטה	Ronald A. Fisher	Cover, T.M. & Hart, P.E.	David Roxbee Cox
סוג	Parametric linear classifier / dimensionality reduction	Instance-based (non-parametric) learning	Method
מקור מכונן	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	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.
כינויים	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	logit model, binomial logistic regression, LR
קשורות	7	5	3
תקציר	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	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.