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Machine learning

Regression and Smoothing Splines

Kujaribu kuunganisha polynomial moja ya shahada ya juu kwa data iliyopinda ni hatari sana — huenda kwa kasi, hasa kwenye kingo. Splines hutatua hili kwa kuvunja safu katika vipande kwenye fundo na kuunganisha polynomial ya shahada ya chini (kawaida ujazo) ndani ya kila moja, huku ikilazimisha vipande kuungana kwa laini — thamani sawa, mteremko, na ukingo katika kila fundo. Matokeo yake ni curve laini ambayo inaweza kufuata muundo wa ndani bila kutokuwa na utulivu wa jumla wa polynomials za shahada ya juu. Spline ya kulainisha huenda zaidi kwa kuweka fundo kwenye kila nukta ya data na badala yake kudhibiti unyumbulifu kupitia adhabu ya ukingo.

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Vyanzo

  1. Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. DOI: 10.1214/ss/1038425655
  2. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0

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ScholarGate. (2026, June 2). Regression and Smoothing Splines. ScholarGate. https://scholargate.app/sw/machine-learning/regression-splines

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ScholarGateRegression Splines (Regression and Smoothing Splines). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/machine-learning/regression-splines · Seti ya data: https://doi.org/10.5281/zenodo.20539026