Quiz demi-supervisé de l'apprentissage

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

1. Njàngale muñ demi-supervisé dañu ko jagleel anam yu:
   • Bépp misaal amna etiket bu mat sëkk
   • Amul benn done
   • Etiket yi yomb nañu lool
   • Am nga done yu néew yuñ etikete ak done yu bari yuñu etiketewul
2. How does self-training use selected predictions on unlabeled examples?
   • It treats selected model guesses as pseudo-labels for further fitting
   • It converts every guess into a verified human annotation
   • It permanently removes the original labeled examples by definition
   • It chooses labels only from alphabetical order
3. Under consistency regularization, what agreement is encouraged across chosen class-preserving augmentations?
   • A requirement that every arbitrary edit preserve the class
   • Opposite predictions for every view of an example
   • Agreement in the model's predictions
   • Removal of every unlabeled input from the loss
4. What does a cluster assumption propose for grouped observations in a semi-supervised method?
   • Every possible cluster must contain every class equally
   • They are likely to share a label under the assumed data structure
   • All semi-supervised methods guarantee identical clusters
   • Labels are determined by filename order
5. naka la ko FixMatch di boolee ñaari xalaat yu mag yi?
   • Dafay pseudo-etiketu ab dugal buñu yokk bu néew doole, ba noppi di tàggat biñu yokk bu baax bi mu méngoo
   • Dafay jëfandikoo done yuñ etikete rek te du bàyyi xel ci leneen lépp
   • Dafay efaase yokk yi yépp
   • Dafay joxe ay etiket ci anam wu amul benn njariñ
6. How can confirmation bias arise during pseudo-label training?
   • Only low-confidence labels can ever be wrong
   • Every selected prediction becomes a verified fact
   • Using any verified label prevents the model from learning
   • Incorrect model-generated labels are accepted and then reinforced
7. Which structure distinguishes graph-based label propagation from repeatedly selecting a classifier's confident guesses?
   • Only a threshold on independently predicted class probabilities
   • A similarity graph over labeled and unlabeled observations
   • A list of random output labels with no input similarities
   • A requirement to discard every relationship between observations
8. In the stated scikit-learn semi-supervised interface, which integer marks an unlabeled target entry?
   • Zero, necessarily the first actual class label
   • One, necessarily the second actual class label
   • Minus one
   • Fifty, interpreted as a percentage-confidence threshold