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4min chapter
When data leakage turns into a flood of trouble
Practical AI: Machine Learning, Data Science, LLM
How to Avoid Target Leakage in Data Science
A lot of data scientists just use one size fits all, basically, like cross validation is easy because I can just write this like one line in my code. How do you get it out into the real world and accommodate these same issues? It's much better to do it earlier before you have you or your team spending time kind of looking at these models than doing it.
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