Guide-yakabatana mibvunzo · Pakati Nhanho

Logistic Regression Quiz

Inotarisa sigmoid uye softmax zvingangoitika, muchinjika-entropy yakakodzera, logi-odd dudziro, zvikumbaridzo zvesarudzo, uye sarudzo dzekugara.

Nzira dzekutungamira dzinoenderanaLogistic Regression
Mubvunzo 1 ye 8

Pasinei nezita rayo, chii chinonzi logistic regression chinoshandiswa?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Pasinei nezita rayo, chii chinonzi logistic regression chinoshandiswa?
    • Kuronga muzvikamu
    • Kufanotaura nhamba dzinoenderera
    • Kubatanidza data isina kunyorwa
    • Kuderedza zviyero
  2. Chii chinoita sigmoid (logistic) basa mumuenzaniso?
    • Inotora nhamba yezvinhu
    • Inoisa chero kukosha kune mukana uri pakati pe0 ne1
    • Inobvisa kunze
    • Inounganidza data
  3. Ndeipi kurasikirwa basa iro logistic regression inoderedza panguva yekudzidziswa?
    • Kureva kukanganisa kwakapetwa kaviri
    • Inertia
    • Log kurasikirwa (cross-entropy)
    • Hinge kurasikirwa
  4. Sei kudzoreredzwa kwemaitiro kuchikosheswa pakududzira?
    • Haishandisi masvomhu
    • Inovanza pfungwa dzayo dzemukati
    • Inogara ichifanotaura 50 muzana
    • Huremu hwega hwega hunoratidza kuti chinhu chinoshandura sei maodhi emugumisiro
  5. Ko logistic regression inoshandura sei mukana kuita kirasi label?
    • Inoshanda pachikumbaridzo, kazhinji 0.5
    • Inogara inotora kirasi yekutanga
    • Inoita pakati pevavakidzani
    • Inoshandisa kusarudzwa kwemaitiro
  6. Ko logistic regression inobatanidza sei kune neural network?
    • Hazvina hukama navo
    • A neural net's final sigmoid kana softmax layer ndeyekurongeka kwekugadzirisa
    • Inotsiva ese akavanzika akaturikidzana
    • Inogona chete kumhanya paGPUs
  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