विधियों की तुलना करें

	स्टैकिंग	लॉजिस्टिक रिग्रेशन	सपोर्ट वेक्टर मशीन (वर्गीकरण)
क्षेत्र	मशीन अधिगम	अनुसंधान सांख्यिकी	मशीन अधिगम
परिवार	Machine learning	Process / pipeline	Machine learning
उद्भव वर्ष	1992	1958	1995
प्रवर्तक	Wolpert, D.H.	David Roxbee Cox	Cortes, C. & Vapnik, V.
प्रकार	Ensemble (heterogeneous meta-learning)	Method	Maximum-margin classifier (kernel method)
मौलिक स्रोत	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
उपनाम	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	logit model, binomial logistic regression, LR	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
संबंधित	5	3	5
सारांश	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.	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.