quiz bu lëkkale ak tegtal · Diggante Niveau

Laaj ak tontu ci régression logistik

Dafay saytu probabilite sigmoid ak softmax, méngoo entropi cross, tekki log-odds, dogal yi, ak tànneefi yamale.

Yooni njiit yu jëm ciRegresioŋ logistik
Laaj 1 de 8

Doonte turam noonu la, lan mooy régression logistic?

Xoolaatal laaj bu nekk8 laaj

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

  1. Doonte turam noonu la, lan mooy régression logistic?
    • Séddale ci kategori
    • Xalaatal lim yu wéy
    • Clustering done yu amul etiketu
    • Wàññi dimension yi
  2. Lan la fonction sigmoïde (logistik) di def ci model bi?
    • Dafay tànn limu man-man yi
    • Dafay méngale bépp valeur ak probabilite bu nekk ci diggante 0 ak 1
    • Dafay dindi mbir yu génn
    • Dafay boole done yi
  3. Ban fonction perte la régression logistic di wàññi ci diiru tàggat yaram?
    • Jumtukaay kaare moyen
    • Inersie
    • Log perte (entropie croix)
    • Perte de charnière
  4. Lan moo tax ñuy jox solo régression logistic ngir mëna tekki?
    • Jëfandikuwul math
    • Dafay nëbb logique bi ci biir
    • Dafay wax luy waaja am 50 pursaa
    • Poids bu nekk dafay wane ni man-man bi di soppi log-odds yi ci resultaa bi
  5. Naka la regresioŋ logistik di soppi probabilite ci etiketu klaas?
    • Dafay tëral benn threshold, lu bari 0.5
    • Saa yu nekk dafay tànn klaas bu njëkk bi
    • Dafay xayma dëkkandoo yi
    • Dafay jëfandikoo tànneef yu bari
  6. naka la regresioŋ logistik di lëkkaloo ak reso neuronal yi?
    • Amul benn lëkkaloo ak ñoom
    • Sigmoid wala couche softmax bu mujj ci reso neuronal mooy régression logistik
    • Dafay wecci bépp diisaay bu nëbbu
    • Mën na dox ci GPU yi kese
  7. What does convexity tell us about the binary logistic-regression negative log-likelihood?
    • It guarantees exactly one finite solution for every dataset
    • It makes the model immune to mislabeled examples
    • A finite local minimum is global, but convexity alone does not guarantee uniqueness
    • It means the decision boundary must be nonlinear
  8. What can complete separation imply for unregularized logistic-regression maximum likelihood?
    • The model must misclassify every training example
    • The sigmoid stops mapping scores to probabilities
    • The optimizer automatically adds regularization
    • There may be no finite maximum-likelihood estimate because coefficients can grow without bound