Idarudapọ Matrices adanwo

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

1. Ninu matrix iporuru alakomeji, kini Negetifu Eke?
   • Ọran ti o daadaa awoṣe ti ko tọ ni asọtẹlẹ bi odi
   • Odi ti o tọ sọtẹlẹ bi odi
   • Ti sọ asọtẹlẹ odi ti ko tọ bi rere
   • Iṣeduro ti o tọ ni asọtẹlẹ bi rere
2. Iṣiro deede jẹ iṣiro bi:
   • TP / (TP + FN)
   • TP / (TP + FP)
   • TN/ (TN + FP)
   • (TP + TN) / lapapọ
3. Metiriki wo ni o ṣe iwọn ipin ti awọn idaniloju gidi ti awoṣe ṣe idanimọ ni deede?
   • Itọkasi
   • Ni pato
   • Ranti
   • Yiye
4. Ninu matrix iporuru fun iṣoro pẹlu awọn kilasi 5, kini awọn sẹẹli diagonal duro?
   • Awọn asọtẹlẹ ti ko tọ
   • Awọn idaniloju eke nikan
   • Lapapọ nọmba ti awọn ayẹwo
   • Awọn asọtẹlẹ ti o tọ fun kilasi kọọkan
5. Kini idi ti iṣedede nikan le jẹ ṣinilọ laisi matrix iporuru kan?
   • O tọju iwọntunwọnsi laarin awọn iru aṣiṣe oriṣiriṣi bii awọn idaniloju eke ati awọn odi eke
   • Yiye jẹ aṣiṣe nigbagbogbo
   • Ko le ṣe iṣiro fun awọn iṣoro alakomeji
   • O nilo awọn iṣeeṣe dipo awọn akole
6. Dimegilio F1 jẹ itumọ ibaramu eyiti awọn metiriki meji?
   • Yiye ati ni pato
   • Konge ati ÌRÁNTÍ
   • Ifamọ ati ni pato
   • Awọn idaniloju otitọ ati awọn odi otitọ
7. In a binary confusion matrix, which count is a False Positive?
   • An actual positive that the model predicts as positive
   • An actual positive that the model predicts as negative
   • An actual negative that the model predicts as positive
   • An actual negative that the model predicts as negative
8. Why should you check a library's confusion-matrix axis labels before calculating precision or recall?
   • The diagonal always contains false positives
   • Axis order changes the number of observations in the dataset
   • Precision and recall use no confusion-matrix cells
   • Libraries may place true classes in rows and predicted classes in columns, or use a different orientation