Final answer:
To prevent data leakage, it is crucial to remove irrelevant data, preprocess data outside cross-validation folds, discard variables not available in production, and perform a validation set sanity check.
Additional measures include using accuracy nudges, differential privacy, checking for lurking variables, blinding in studies, and ensuring data privacy and informed consent.
Therefore, all the options are correct.
Step-by-step explanation:
To help prevent a data leakage situation, several good practices could be implemented.
First, it is advisable to remove any irrelevant data that does not pertain to the event of interest, ensuring that only necessary information is processed and potentially exposed.
Secondly, it's crucial to ensure that data is preprocessed outside of any cross-validation folds. This action helps prevent any inadvertent leakage between the training and testing datasets.
Thirdly, you should remove variables that a model in production would not have access to, thus avoiding the risk of 'data snooping' where the model has information it should not.
Lastly, a sanity check of the model with an unseen validation set can reveal whether the model has been influenced by subtle data leakage and assess its true predictive power.
Other ways to prevent data leakage include using accuracy nudges to help identify false information, granting researchers access to more data with safeguards like differential privacy, checking for and accounting for lurking variables, and considering the use of blinding during studies to prevent bias.
Protecting individuals' data and ensuring informed consent for data use are fundamental yet challenging aspects to verify in practice. Such measures are important for maintaining data integrity and protecting the privacy of data subjects.