Feature Engineering for Modern Machine Learning with Scikit-Learn

Answers

1. B) Pipelines ensure transformations are consistently applied to both training and test data.
2. B) To combine multiple transformations applied in parallel into a single dataset.
3. B) A technique to select the most important features by recursively removing the least impactful features.
4. C) When the dataset has a significant class imbalance.
5. C) It generates synthetic samples by interpolating between existing minority samples.
6. B) Time-Series Split Cross-Validation
7. B) Accuracy does not account for model bias toward the majority class.
8. B) F1 Score
9. B) SMOTE can be applied in each cross-validation fold using a pipeline to balance classes in each fold.
10. B) To ensure feature engineering steps are applied consistently across training and test data.