LOESS / LOWESS Usanifu wa Kurekebisha wa Kienyeji
LOESS (locally estimated scatterplot smoothing), iliyoanzishwa na William Cleveland mwaka 1979 na kupanuliwa na Susan Devlin mwaka 1988, huweka mshazari laini kupitia data kwa kufanya urejeshaji wa polynomial wenye uzito tofauti katika maeneo ya kila nukta. Maangalizi ya karibu huhesabiwa zaidi kuliko yale ya mbali, hivyo mbinu hufuata muundo wa kienyeji bila kudhani aina yoyote ya utendaji wa jumla, na kuifanya kuwa kipolezo maarufu cha uchunguzi kwa michoro ya kutawanya.
Soma mbinu kamili
Ingia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. DOI: 10.1080/01621459.1979.10481038 ↗
- Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596–610. DOI: 10.1080/01621459.1988.10478639 ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 2). Local Regression (LOESS / LOWESS). ScholarGate. https://scholargate.app/sw/machine-learning/loess
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.
- Generalized Additive Model (GAM)Ujifunzaji wa Mashine↔ compare
- Regressioni ya PolinomialiTakwimu↔ compare
- Regression SplinesUjifunzaji wa Mashine↔ compare
Imerejelewa na
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