How can I prevent data leakage in my machine learning model?

How can I prevent data leakage in my machine learning model?

To prevent data leakage, ensure that your training and testing datasets are properly separated and that no information from the test set is used during training.

What are some common sources of data leakage?

Common sources include using future data in training, improper cross-validation, and including features that are derived from the target variable.

Can you give me an example of data leakage?

Sure! An example is when a model is trained on a dataset that includes a feature that directly correlates with the target variable, like using a timestamp in predicting an event that occurs after that timestamp.

How can I identify if my model has data leakage?

You can identify data leakage by checking for unusually high performance metrics during validation, which may indicate that the model has learned from leaked information.