ROC ekoro ati AUC adanwo

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

1. Awọn iwọn meji wo ni ohun ti tẹ ROC ṣe idite si ara wọn?
   • Odiwọn Rere Tòótọ la
   • Konge vs ÌRÁNTÍ
   • Yiye vs. Ala
   • Ifamọ vs konge
2. Ohun ti AUC iye ni ibamu si a classifier ti o ṣe ko dara ju ID lafaimo?
   • 0.0
   • 0.5
   • 0.75
   • 1.0
3. AUC le ṣe tumọ bi iṣeeṣe pe awoṣe:
   • Titọ lẹsẹsẹ ayẹwo ti a yan laileto
   • Ṣe aṣeyọri ju 90% deede
   • Awọn Dimegilio a ID rere ti o ga ju a ID odi
   • Ni o ni odo eke rere
4. Kini idi ti AUC ṣe apejuwe bi 'ominira ala'?
   • O ṣiṣẹ nikan ni ẹnu-ọna 0.5 kan
   • O foju pa kilasi rere patapata
   • O nilo awọn iṣeeṣe iwọn
   • O ṣepọ iṣẹ ṣiṣe kọja gbogbo awọn ala ti o ṣeeṣe
5. Fun data ti ko ni iwọntunwọnsi pupọ, iru ọna wo ni a ṣe iṣeduro nigbagbogbo lẹgbẹẹ tabi dipo ROC nitori ROC le wo ireti aṣeju?
   • Konge-RÁNTÍ ti tẹ
   • Eko eko
   • Iwọn odiwọn
   • Ipilẹ ipadanu
6. Ojuami kan ni igun apa osi ti aaye ROC duro fun:
   • Awọn idaniloju eke ti o ga ati awọn idaniloju otitọ kekere
   • Pipe tabi sunmọ-pipe Iyapa
   • ID išẹ
   • Awoṣe ti ko tọ
7. Why can a model have a high ROC-AUC but poorly calibrated probability estimates?
   • AUC directly measures the distance between every predicted probability and its true label
   • Calibration is guaranteed whenever the ROC curve is above the diagonal
   • AUC depends on score ranking, so a monotonic score transformation can preserve ranking while changing probability values
   • AUC uses only one fixed classification threshold
8. What does partial AUC let an evaluator emphasize?
   • Only the model's training loss
   • Probability calibration across all score values
   • The number of labels in a multiclass model
   • Performance in a selected false-positive-rate region, such as the low-FPR range