Usajili wa Viwango vya chini vya sehemu (PLS)
Usajili wa viwango vya chini vya sehemu (Partial least squares regression) hutabiri mwitikio kutoka kwa vigezo vingi, mara nyingi vilivyounganishwa sana, kwa kuvipanga kwenye seti ndogo ya vipengele fiche — lakini, tofauti na usajili wa vipengele vikuu (principal components regression), huchagua vipengele hivyo ili kuongeza ushirikiano wake na mwitikio, si tu utofauti wa vigezo. Upunguzaji huu wa vipimo unaosimamiwa na mwitikio (supervised dimension reduction) hufanya PLS kuwa zana muhimu katika kemia ya uchanganuzi (chemometrics), upimaji wa mionzi (spectroscopy), na mazingira mengine yenye data pana ambapo vigezo vingi huzidi idadi ya maangalizi.
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
- Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. DOI: 10.1016/S0169-7439(01)00155-1 ↗
- Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1–17. DOI: 10.1016/0003-2670(86)80028-9 ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 2). Partial Least Squares Regression (PLS). ScholarGate. https://scholargate.app/sw/machine-learning/partial-least-squares
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.
- Msawazo Mkuu wa Mlinganyo (MLR)Takwimu↔ compare
- Regression ya vipengele vikuu (PCR)Ujifunzaji wa Mashine↔ compare
- Regressioni ya MtepeUjifunzaji wa Mashine↔ compare
Imerejelewa na
Umeona tatizo kwenye ukurasa huu? Ripoti au pendekeza marekebisho →