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	एन्सेम्बल के-नियरेस्ट नेबर्स (Ensemble K-Nearest Neighbors)	बैगिंग (बूटस्ट्रैप एग्रीगेटिंग)
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2000s	1996
प्रवर्तक	Domeniconi, C. & Yan, B. (key formalization)	Breiman, L.
प्रकार	Ensemble (aggregated KNN classifiers/regressors)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
मौलिक स्रोत	Domeniconi, C., & Yan, B. (2004). Nearest neighbor ensemble. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Vol. 1, pp. 228–231. IEEE.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
उपनाम	Ensemble KNN, KNN ensemble, aggregated k-nearest neighbors, combined KNN	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
संबंधित	5	5
सारांश	Ensemble K-Nearest Neighbors combines multiple KNN models — each trained with a different value of k, distance metric, feature subset, or data bootstrap — and aggregates their predictions by majority vote (classification) or averaging (regression). The approach reduces the high variance inherent in any single KNN model and produces more stable, accurate predictions on tabular data.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.