가이드 연계 퀴즈 · 쉬움 레벨

머신러닝 기초 퀴즈

실용적인 기계 학습 작업을 분류하고, 기준선을 비교하고, 테스트 데이터를 보호하고, 평균으로 숨길 수 있는 일반화 또는 배포 실패를 찾아냅니다.

관련 가이드 경로머신러닝 기초
질문 1 의 8

A store wants to estimate next week’s sales as a dollar amount from past sales and calendar features. Which task is this?

모든 질문 미리보기8 질문

퀴즈를 풀면 정답과 설명이 나타납니다.

  1. A store wants to estimate next week’s sales as a dollar amount from past sales and calendar features. Which task is this?
    • Regression
    • 분류
    • Clustering
    • Text generation
  2. A support team assigns each incoming message to one of five known issue labels. Which task matches that goal?
    • Clustering
    • 분류
    • Regression
    • Dimensionality reduction
  3. An analyst groups unlabeled news articles by similarity without predefined topics. Which task is being used?
    • 분류
    • Regression
    • Clustering
    • Forecasting
  4. A model scores 98% on training records but performs poorly on new records. What is the main concern?
    • It has too few input columns by definition
    • It has proved that the labels are correct
    • It is necessarily a clustering model
    • It may fit the training examples well without generalizing to new inputs
  5. Why should a team keep a final test set out of repeated model tuning?
    • Repeated tuning can adapt decisions to the test set, weakening it as an independent check
    • The test set is only for fitting model parameters
    • A test set prevents selecting a metric
    • The training set must always be smaller
  6. A forecasting team compares a complex model with “use yesterday’s value” before adopting it. What purpose does that simple rule serve?
    • A substitute for monitoring after deployment
    • A baseline showing whether added complexity provides useful information
    • A way to relabel regression as classification
    • A guarantee that the complex model is fair
  7. A deployed model suddenly receives temperatures in Fahrenheit after training on Celsius. Which workflow lesson applies?
    • Strong test accuracy makes input units irrelevant
    • Only the model architecture can cause failures
    • Errors can arise from production preparation or changed input conventions
    • Deployment removes the need to check inputs
  8. A classifier has strong average accuracy but misses nearly every rare emergency case. What should the team do?
    • Accept it because average accuracy is high
    • Tune on the final test set until the score improves
    • Remove the rare cases from evaluation
    • Inspect failure cases and choose metrics that reflect the consequences of mistakes