השוואת שיטות

	רגרסיה לוגיסטית	ערימה
תחום	סטטיסטיקה למחקר	למידת מכונה
משפחה	Process / pipeline	Machine learning
שנת המקור	1958	1992
הוגה השיטה	David Roxbee Cox	Wolpert, D.H.
סוג	Method	Ensemble (heterogeneous meta-learning)
מקור מכונן	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
כינויים	logit model, binomial logistic regression, LR	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
קשורות	3	5
תקציר	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.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.