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AI の学習方法に関するクイズ

教師あり学習、教師なし学習、強化学習、さらに過学習、テスト セット、モデルの損失が実際に測定するものについて理解していることを確認してください。

関連するガイドパスAIはどのように学習するのか
質問 1 の 8

教師あり学習では、トレーニング データには何が含まれますか?

すべての質問をプレビューする8 質問

クイズに答えると、答えと解説が表示されます。

  1. 教師あり学習では、トレーニング データには何が含まれますか?
    • Only raw examples with no answers attached
    • Rules written by programmers for every possible situation
    • Inputs paired with target outputs, called labels
    • Rewards and penalties from a game environment
  2. 事前定義されたカテゴリを使用せずに、類似した買い物習慣によって顧客をグループ化することは、どのタイプの学習の例ですか?
    • 教師なし学習
    • Rule-based programming
    • Supervised learning
    • 強化学習
  3. 強化学習エージェントは、ビデオ ゲームのプレイを学習するエージェントと同様に、どのように改善されるのでしょうか?
    • By trying actions and learning from rewards or penalties
    • By memorizing the game's source code
    • By copying labeled examples showing the correct move each turn
    • By sorting past games into clusters without feedback
  4. A model performs well on its training examples but poorly on comparable held-out examples. Which pattern does this show?
    • アンダーフィッティング
    • 過学習
    • データ漏洩
    • Good generalization
  5. Why keep a test set separate from fitting the model and choosing its settings?
    • To estimate performance on examples not used for those development choices
    • To tune hyperparameters by repeatedly checking the final evaluation set
    • To add held-out examples to training after the choices are final
    • To check whether training predictions match their training labels
  6. In the guide’s supervised training loop, what does the loss measure?
    • How much electricity the training run uses
    • How long the model takes to give an answer
    • How many examples were deleted from the training dataset
    • The numerical difference between predictions and target values
  7. A text model learns to predict the next token from the preceding context. Which learning setup does the guide describe?
    • Supervised learning with separately supplied labels
    • Unsupervised clustering of the text
    • Self-supervised training that creates a prediction task from the text
    • Reinforcement learning from rewards for each token
  8. A model changes its answer after seeing an example in a prompt. What can be concluded about its learned weights?
    • They must have permanently changed
    • The prompt became part of every user’s training data
    • The model cannot use examples without retraining
    • A changed response does not by itself show a weight update