How would you approach the problem of data leakage during the preprocessing and modeling phase of a Machine Learning project?
- Ignore the problem as it has no impact
- Mix the test and training data for preprocessing
- Split the data before any preprocessing and carefully handle information from the validation/test sets
- Use the same preprocessing techniques on all data regardless of splitting
To prevent data leakage, it's crucial to split the data before any preprocessing, ensuring that information from the validation or test sets doesn't influence the training process. This helps maintain the integrity of the evaluation.
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