Hyperparameter Tuning Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Chii chinosiyanisa hyperparameter kubva kune yakajairwa modhi parameter?
   • Hyperparameters inoiswa pamberi pekudzidziswa uye haina kudzidziswa zvakananga kubva kune data
   • Hyperparameters inodzidzwa otomatiki nekudzika kwegradient panguva yekudzidziswa
   • Hyperparameters inongowanikwa mumitsetse mhando
   • Hapana musiyano chaiwo pakati pavo
2. Ndeipi inowanzoonekwa seyakanyanya kukanganisa hyperparameter kuti iite mukudzidza kwakadzama?
   • Mbeu isina kurongeka
   • Chiyero chekudzidza
   • Iyo faira fomati yedataset
   • Zita re optimizer variable
3. Nei kutsvaga zvisina tsarukano kuchiwanzo pfuura grid kutsvaga hyperparameter tuning?
   • Inogara ichiedza mamwe masanganiswa akazara
   • Izvo hazvidi seti yekusimbisa
   • Iyo sampuli yenzvimbo zvakanyatsonaka, kuwana yakanaka hunhu pasina kupedzisa kuvhara yega yega grid point
   • Inodzidza huremu uye hyperparameters panguva imwe chete
4. Ko Bayesian optimization inosarudza sei hyperparameter kumisikidzwa yekuyedza inotevera?
   • Iyo inotora zvigadziriso zvachose pane zvakasarudzika nguva dzese
   • Iyo inogara ichitora iyo yekumisikidza ine akawanda maseru
   • Inokopa marongero emuyedzo wekare chaizvo
   • Iyo inovaka modhi yezvigadziriso zvinotaridzika zvinovimbisa uye zviyero zvekuongorora kusavimbika nekushandisa mhedzisiro yakanaka.
5. Nei iyo yekupedzisira bvunzo seti ichifanira kuramba isina kubatwa panguva ye hyperparameter tuning?
   • Nekuti kugadzirisa pamusoro payo kunoburitsa ruzivo uye kupa fungidziro yekufungidzira kwekuita chaiko
   • Nekuti test set inoshandiswa kuverengera huremu
   • Nekuti test set inogara idiki pane yekusimbisa set
   • Nekuti hyperparameter inochengetwa muyedzo set
6. Nderipi dambudziko rinogadziriswa nekutanga-kumisa chirongwa senge Hyperband mu hyperparameter tuning?
   • Inobvisa kudiwa kwechiyero chekudzidza
   • Iyo inochengetedza komputa nekuuraya isingaite miyedzo yekutanga uye nekupa zviwanikwa kune vanovimbisa
   • Iyo inovimbisa iyo modhi haizopfuurire
   • Iyo inoshandura hyperparameter kuita inodzidzika paramita
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