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Urejeshaji Linear Uliodhibitiwa

Urejeshaji linear uliodhibitiwa huongeza kipengele cha adhabu kwenye lengo la mraba mdogo wa kawaida, kikipunguza au kuweka sifuri vizio ili kupunguza uwekaji-zaidi (overfitting) na kushughulikia ushirikiano-mwingi (multicollinearity). Aina kuu tatu — Ridge (adhabu ya L2), Lasso (adhabu ya L1), na Elastic Net (mchanganyiko wa L1+L2) — hufanya urejeshaji linear kutumika hata wakati vipengele vinazidi idadi ya uchunguzi au vitabiri vimeunganishwa sana.

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Vyanzo

  1. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x
  2. Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Regularized Linear Regression (Ridge, Lasso, Elastic Net). ScholarGate. https://scholargate.app/sw/machine-learning/regularized-linear-regression

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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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Imerejelewa na

ScholarGateRegularized linear regression (Regularized Linear Regression (Ridge, Lasso, Elastic Net)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/machine-learning/regularized-linear-regression · Seti ya data: https://doi.org/10.5281/zenodo.20539026