Kediska Daawaynta Hyperparameter

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Maxaa hyperparameter ka soocaya cabbirka moodeelka caadiga ah?
   • Hyperparameters ayaa la dejiyaa ka hor tababarka oo aan si toos ah looga baran xogta
   • Hyperparameters si toos ah ayaa loo bartaa hoos u dhac tartiib tartiib ah inta lagu jiro tababarka
   • Hyperparameters kaliya ayaa ku jira moodooyinka toosan
   • Ma jiro farqi dhab ah oo u dhexeeya
2. Kee baa inta badan loo tixgaliyaa ka kaliya ee ugu saamaynta badan hyperparameter si loo habeeyo barashada qoto dheer?
   • Abuurka random
   • Heerka waxbarashada
   • Habka faylka ee xogta
   • Magaca doorsoomiyaha optimizer
3. Waa maxay sababta raadinta random-ka ay inta badan uga sarreyso raadinta grid ee hagaajinta hyperparameter?
   • Waxay had iyo jeer isku daydaa isku-darka badan
   • Uma baahna xidhid ansaxinta
   • Waxay si hufan u tijaabisaa booska bannaan, iyada oo heleysa qiyam wanaagsan iyada oo aan si buuxda loo daboolin dhibic kasta
   • Waxay barataa miisaanka iyo hyperparameters isku mar
4. Sidee bay hagaajinta Bayesian u doorataa qaabaynta hyperparameterka si ay u tijaabiso xiga?
   • Waxay si buuxda u doorataa qaabaynta si aan kala sooc lahayn mar kasta
   • Had iyo jeer waxay soo qaadataa qaabeynta lakabyada ugu badan
   • Waxay si sax ah u koobiyaysaa habayntii tijaabadii hore
   • Waxay dhistaa nooc ka mid ah goobaha u muuqda kuwo rajo leh waxayna dheelitiraan sahaminta hubaal la'aanta ka faa'iidaysiga natiijooyin wanaagsan
5. Waa maxay sababta tijaabada ugu dambeysa ay tahay inaan la taaban inta lagu jiro hagaajinta hyperparameter?
   • Sababtoo ah ka-hortagga ka-hortagga waxay daadinaysaa macluumaadka waxayna siinaysaa qiyaas xad-dhaaf ah oo waxqabad dhab ah
   • Sababtoo ah qalabka tijaabada waxaa loo isticmaalaa in lagu xisaabiyo miisaanka
   • Sababtoo ah goobta imtixaanku had iyo jeer way ka yar tahay habka ansaxinta
   • Sababtoo ah hyperparameters ayaa lagu kaydiyaa qaybta tijaabada
6. Dhibaato noocee ah ayuu nidaamka joojinta hore sida Hyperband ku xalliyaa habaynta hyperparameter?
   • Waxay meesha ka saaraysaa baahida heerka waxbarasho
   • Waxay badbaadisaa xisaabinta iyadoo dili doonta tijaabooyinka aan shaqayn xilli hore iyo siinta agabka kuwa ballan qaadaya
   • Waxay dammaanad qaadaysaa in moodelku aanu dhaafi doonin
   • Waxay u beddeshaa hyperparameters oo u beddela cabbirro la baran karo
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