Laaj ak tontu ci ay hiperparametre

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Lan mooy wuutale hyperparamètre ak parametre model buñ miin?
   • Hyperparamètres yi deñukoy def laataa ñuy tàggat te duñu leen jàngee ci done yi
   • Dañuy jàng hyperparametre yi ci saasi jaaraleko ci wàccinu degrade bi ci diiru tàggat yaram
   • Hyperparametre yi nekk nañu ci xeetu ligneer yi kese
   • Amul benn wuute ci seen biir
2. Lan mooy hyperparametre bi gëna am solo ngir mëna jàng bu xóot?
   • Jiwwu yu bari
   • Tolluwaayu jàng
   • Format fichier bu done yi
   • Turu variable biy gëna xéewale
3. Lan moo waral seetlu aleatër di faral di raw seetlu griy ngir akor hyperparametre?
   • Dafay jéema def ay boole yu gëna bari
   • Soxlawul ab setu validasioŋ
   • Dafay sample espace bi ci anam wu gëna xéewale, di gis valeur yu baax te du muur bépp poñ griy
   • Dafay jàng poid yi ak hiperparametre yi benn yoon
4. naka la ko optimaasioŋ bayesien di tànnee ban taabal hiperparametre lay jéem ci topp?
   • Dafay tànnee configurations yi ci anam wu mucc saa yu nekk
   • Saa yu nekk dafay tànn configuration bi gëna bari diisaay
   • Dafay kopie jekkal àtte ba njëkk
   • Dafay tabax xeetu jekkal bu nuru lu am njariñ ak ekilibre banneexu lu wóorul ak jëfandikoo njariñ yu baax
5. Lan moo waral ñu wara baña laal ensemble test bu mujj bi ñuy ajuste hyperparamètre bi?
   • Ndax suñu ko méngale ak loolu dina sacc xibaar ba noppi joxe xayma bu ëpp yaakaar ci performance dëgg
   • Ndax test bi lañuy jëfandikoo ngir xayma poid yi
   • Ndax ensemble test bi dafay faral di gëna ndaw ensemble validation bi
   • Ndax ay hiperparametre dañu leen denc ci test bi
6. Ban jafe-jafe la xeetu taxawal bu teel bu melni Hyperband di saafara ci ajustement hyperparamètre?
   • Dina dindi soxla taxawaayu jàng
   • Dafay sakkanal ordinatër ndax dafay teela ray test yu baaxul yi ba noppi jox jumtukaay ñi am yaakaar
   • Dafay garanti ni model bi du ëpp loxo
   • Dafay soppi hiperparametre yi ci paramet yuñ mëna jàng
7. In Bayesian hyperparameter optimization, what role does the probabilistic surrogate model play?
   • It replaces training every candidate with a guaranteed exact score
   • It updates model weights during the final test
   • It estimates how promising configurations may be and guides the next trial
   • It labels the training examples
8. Why should tuning trials be compared on a validation set rather than the final test set?
   • The test set cannot contain labels
   • The validation set is always larger
   • The model cannot predict on a test set
   • Repeated test-set feedback would leak information and make the final estimate overoptimistic