When data leakage turns into a flood of trouble
Practical AI
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Understanding Target Leakage in Data Science
This chapter explores the concept of target leakage in predictive models and its potential to skew results due to the unintentional inclusion of future data. Emphasizing real-world examples such as the impact of biased data handling and improper data partitioning, the discussion advocates for best practices in model development. It also highlights the significance of effective collaboration and documentation in preventing data leakage and ensuring robust model performance.
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