quiz bu lëkkale ak tegtal · Diggante Niveau
Quiz buy saytu
Saytu ni validasioŋ bu sukkandiko ci fold di xaymaa performance model bi, moytu fuit, ak tàqale tuning hyperparameter ak jàngat bu mujj bi.
Laaj 1 de 8
Ci k-fold cross-validation buñ miin, ñaata yoon lañuy jëfandikoo poñ done bu nekk ngir saytu?
Xoolaatal laaj bu nekk8 laaj
Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.
- Ci k-fold cross-validation buñ miin, ñaata yoon lañuy jëfandikoo poñ done bu nekk ngir saytu?
- Benn yoon
- k yoon
- k-1 yoon
- Dafay aju ci jiwwu yiñ tànn
- Lan mooy njariñ li gëna mag ci k-fold cross-validation ci benn xaaj saxaar/test?
- Saa yu nekk dafay yokk njubteg model bi
- Dafay joxe xayma bu gëna dëgër te baña aju ci wërsëg
- Dina dindi bépp soxla def test set
- Dafay gaawal tàggat yaram
- Validaasioŋ buñ jagleel k-fold dafa am njariñ bu bari sudee:
- Done yi dañu ekilibre bu baax
- Done yi ay sérii waxtu lañu
- Am nga ay klaas yu tolloowul te bëgg nga fold bu nekk am limu klaas bu noonu mel
- Danga bëgga tàggat ci done yépp benn yoon
- Bàyyi-benn-ci biti cross-validation (LOOCV) dafay def limu pli k mu tollu ci:
- 2
- 10
- Limu man-man yi
- Limu misaal yi ci done yi
- Ngir moytu ay done di jaar ci diggante cross-validation, jéego yi ñuy njëkka def lu melni scaling man-man yi dañu wara nekk:
- Fit ci biir pli tàggat bu nekk, suko defee nga jëfandikoo ko ci pli validasioŋ
- Fit ci dataset bi yépp balaa ngay xaaj ay pli
- Dañu ko sànni lépp
- Def ci pli validasioŋ yi kese
- Lan moo waral k-fold cross-validation bu jaar yoon jaarul yoon ngir seetlu série temps?
- Seri waxtu bariwul ay ligne
- Pli yu bari mën nañu def ay done yu ëlëg ci ensemble tàggat bi ñuy jëfandikoo ngir seetlu li weesu
- Dafa gaaw lool ci seeni serie
- Mënu xaaj série waxtu
- What is the purpose of nested cross-validation?
- It makes every model use the same hyperparameters
- It lets preprocessing use the full dataset before splitting
- It separates hyperparameter selection from an outer evaluation of the selected procedure
- It removes the need for held-out evaluation
- Which fold counts are common starting choices for k-fold cross-validation?
- Exactly two folds in every problem
- One fold regardless of sample size
- One thousand folds for every dataset
- Five or ten folds