Click here to view the next lesson.

Quiz Part 2: Integration with Scikit-Learn for Model Building

Answers

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

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

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

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