Функциональная инженерная викторина

Ответы и пояснения появляются по мере прохождения теста.

1. Какую работу включает в себя проектирование функций перед обучением модели?
   • Выбор самого быстрого графического процессора для обучения
   • Написание функции потерь модели
   • Преобразование необработанных данных в информативные входные данные, на которых модель может учиться.
   • Развертывание модели в производство
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. Что из этого является типичным методом проектирования признаков для категориальных данных?
   • Увеличение скорости обучения
   • Одно-горячее или целевое кодирование
   • Добавление большего количества графических процессоров
   • Перетасовка имен файлов
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?
   • Семь
   • Один
   • Twenty-five
   • Twelve