ScholarGate
Msaidizi
Machine learningMachine learning

Miti ya Ziada

Miti ya Ziada (Miti Iliyoimarishwa Sana), iliyoletwa na Geurts, Ernst, na Wehenkel mwaka 2006, ni kundi la miti ya uamuzi ambayo huongeza ubadilishaji zaidi kuliko Random Forest. Vipengele vya mgombea na vizingiti vya kugawanyika huchaguliwa kwa nasibu kabisa kwenye kila fundo, kuondoa utafutaji wa ulafi juu ya vizingiti. Ubadilishaji huu wa ziada hupunguza utofauti, mara nyingi unalingana au unazidi usahihi wa Random Forest, na huendeshwa kwa kasi zaidi wakati wa mafunzo.

Fungua katika MethodMindHivi karibuniVideoHivi karibuniDownload slides

Soma mbinu kamili

Kwa wanachama pekee

Ingia kwa akaunti ya bure ili kusoma sehemu hii.

Ingia

Method map

The neighbourhood of related methods — select a node to explore.

+1 more

Vyanzo

  1. Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI: 10.1007/s10994-006-6226-1
  2. Extra-Trees. Wikipedia. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Extremely Randomized Trees (Extra-Trees). ScholarGate. https://scholargate.app/sw/machine-learning/extra-trees

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.

Compare side by side

Imerejelewa na

ScholarGateExtra Trees (Extremely Randomized Trees (Extra-Trees)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/machine-learning/extra-trees · Seti ya data: https://doi.org/10.5281/zenodo.20539026