Linganisha mbinu

	Ensemble Logistic Regression	Kikundi cha Kura (Voting Ensemble)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1996–2000s	1990s–2004
Mwanzilishi	Breiman, L. (bagging); broader ensemble literature	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Aina	Ensemble of logistic regression classifiers	Ensemble (combination of multiple classifiers by vote)
Chanzo asilia	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Majina mbadala	logistic regression ensemble, bagged logistic regression, aggregated logistic regression, logistic ensemble classifier	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Zinazohusiana	6	5
Muhtasari	Ensemble Logistic Regression trains multiple logistic regression classifiers on varied subsets or perturbations of the training data and combines their probability estimates by averaging or voting. The approach preserves logistic regression's probabilistic interpretability while reducing variance and improving predictive stability through aggregation.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.