Logistic ifaseyin adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Pelu orukọ rẹ, kini ipadasẹhin logistic ni otitọ ti a lo fun?
   • Pipin si awọn ẹka
   • Asọtẹlẹ lemọlemọfún awọn nọmba
   • Iṣakojọpọ data ti ko ni aami
   • Idinku awọn iwọn
2. Kini iṣẹ iṣẹ sigmoid (awọn eekaderi) ṣe ninu awoṣe?
   • O yan nọmba awọn ẹya ara ẹrọ
   • O ṣe afihan iye eyikeyi si iṣeeṣe laarin 0 ati 1
   • O yọ awọn outliers kuro
   • O ṣe akojọpọ data naa
3. Iṣẹ ipadanu wo ni isọdọtun logistic dinku lakoko ikẹkọ?
   • Aṣiṣe onigun mẹrin tumọ si
   • Inertia
   • Pipadanu akọọlẹ (agbelebu-entropy)
   • Pipadanu mitari
4. Kini idi ti ipadasẹhin logistic ṣe idiyele fun itumọ?
   • Ko si isiro
   • O hides awọn oniwe-ti abẹnu kannaa
   • O nigbagbogbo sọ asọtẹlẹ 50 ogorun
   • Iwọn iwuwo kọọkan fihan bi ẹya kan ṣe n yi awọn aidọgba wọle ti abajade naa pada
5. Bawo ni ipadasẹhin logistic ṣe maa n yi iṣeeṣe kan pada si aami kilasi kan?
   • O kan ala, nigbagbogbo 0.5
   • Nigbagbogbo o yan kilasi akọkọ
   • O apapọ awọn aladugbo
   • O nlo yiyan laileto
6. Bawo ni ipadasẹhin logistic ṣe sopọ si awọn nẹtiwọọki nkankikan?
   • Ko ni ibatan si wọn
   • Nẹtiwọọki ti o kẹhin sigmoid tabi Layer softmax jẹ ipadasẹhin logistic ni pataki
   • O rọpo gbogbo farasin fẹlẹfẹlẹ
   • O le nikan ṣiṣẹ lori GPUs
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