A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block: A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model. Which of the following is a negative consequence of the approach suggested by the colleague?
Select an option, then click Submit answer.
- ○ The model will take longer to train for each unique combination of hyperparameter values
- ○ The feature engineering stages will be computed using validation data
- ○ The cross-validation process will no longer be parallelizable
- ○ The cross-validation process will no longer be reproducible
- ○ The model will be refit one more per cross-validation fold
