Bagging (Bootstrap Aggregating)
Bagging, kifupi cha Bootstrap Aggregating, ni meta-algorithm ya pamoja iliyoanzishwa na Leo Breiman mwaka 1996 ambayo hufunza nakala nyingi za mwanafunzi msingi kwenye sampuli za bootstrap zilizochorwa kwa uhuru kutoka kwa data ya mafunzo na huunganisha utabiri wao — kwa kuchukua wastani kwa ajili ya kurejesha au kura nyingi kwa ajili ya uainishaji — ili kutoa kiashiria cha mwisho chenye upungufu wa utofauti kwa kiasi kikubwa kuliko mwanafunzi mmoja msingi.
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Method map
The neighbourhood of related methods — select a node to explore.
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Vyanzo
- Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI: 10.1007/BF00058655 ↗
- Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 8.7). Springer. ISBN: 978-0-387-84857-0
- James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 8.2). Springer. ISBN: 978-1-4614-7138-7
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). Bagging (Bootstrap Aggregating). ScholarGate. https://scholargate.app/sw/machine-learning/bagging
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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