特徴量エンジニアリングに関するクイズ

クイズに答えると、答えと解説が表示されます。

1. モデルのトレーニングの前に、特徴量エンジニアリングにはどのような作業が含まれますか?
   • トレーニング用に最速の GPU を選択する
   • モデルの損失関数を書く
   • 生データをモデルが学習できる有益な入力に変換する
   • モデルを本番環境にデプロイする
2. Why should a proposed engineered feature be evaluated with the chosen model and task?
   • Every extra feature guarantees higher accuracy
   • Its usefulness depends on what it represents and how the model uses it
   • Input representation never affects predictions
   • Changing features always matters more than every algorithm choice
3. A feature uses an outcome recorded only after the prediction is needed. Which feature-engineering problem does this illustrate?
   • A necessary improvement in causal identification
   • A harmless change to a file extension
   • A requirement to use more graphics hardware
   • Prediction-time information leakage
4. When fitting a standardization step for a train-and-held-out evaluation, which data should supply its mean and scale?
   • The training data, with those fitted statistics reused on held-out inputs
   • The test data only
   • All data after examining final test outcomes
   • A different refit on every evaluation example
5. カテゴリカル データに対する典型的な特徴量エンジニアリング手法は次のうちどれですか?
   • 学習率の向上
   • ワンホットまたはターゲットエンコーディング
   • GPU を追加する
   • ファイル名をシャッフルする
6. With OneHotEncoder configured as handle_unknown ignore and no dropped category, how is an unseen category represented for its feature?
   • An extra indicator column created automatically for that new category
   • An immediate error regardless of the stated ignore setting
   • All zeros in the indicator columns for that feature
   • The outcome target value copied into the indicator columns
7. Why can extreme numeric observations affect a StandardScaler transformation?
   • Its estimated mean and variance are sensitive to outliers
   • It uses medians and interquartile ranges instead of means and variances
   • It ranks observations without using their numeric magnitudes
   • It clips every value to fitted quantiles before estimating scale
8. In a stated degree-two interaction feature x1 times x2, what is its value for x1 three and x2 four?
   • セブン
   • 1 つ
   • Twenty-five
   • 12