텍스트 분류 퀴즈

퀴즈를 풀면 정답과 설명이 나타납니다.

1. How does a text classifier structure the output for an input document?
   • By necessarily translating it into another language
   • By assigning one or more predefined category labels
   • By generating only a new unrelated paragraph
   • By changing its compressed file format
2. 다중 라벨 분류의 예는 어떤 시나리오인가요?
   • 이메일을 스팸 또는 스팸 아님으로 표시
   • 티켓을 정확히 하나의 부서로 라우팅하기
   • 단일 별점 선택
   • 기사에 '정치'와 '경제'를 동시에 태그하기
3. On a text dataset dominated by one class, what can a high overall accuracy conceal?
   • An inability to calculate any metric
   • Proof that recall equals one for every category
   • Failure to identify cases of a rare class
   • A guarantee that every minority example is correct
4. 분류 작업에서 회상은 무엇을 측정합니까?
   • 시스템이 성공적으로 포착한 실제 사례 수
   • 실제로 올바른 신고 항목 수
   • 모델 실행 속도
   • 모델의 크기
5. In task-specific zero-shot text classification, what is absent for the new classification task?
   • All model pretraining of any kind
   • Every description of the candidate categories
   • The ability to use any learned representation
   • Labeled training examples for that task
6. How can learned text embeddings contribute to a classification pipeline?
   • They guarantee perfect predictions on every new domain
   • They provide numerical features used to compute label scores
   • They delete every grammatical distinction by definition
   • They replace all label definitions with filenames
7. A stated text-classification evaluation has six true positives and two false positives. What is precision for the designated positive class?
   • 75 percent
   • 25 percent
   • 100퍼센트
   • Six percent
8. Which output-score design permits several text labels to score highly at once rather than requiring scores to sum to one?
   • A single softmax over mutually exclusive classes
   • A rule requiring exactly one positive label
   • A separate sigmoid output for each label
   • A filename-only representation without label scores