Introduction
Many models have good scores but cannot survive in real environments for a very basic reason: data leakage. This article is extremely important, because if you get it wrong here, all the previous beautiful metrics are almost worthless.
Lesson objectives
- Identify common types of leakage.
- Know how to do error analysis to understand where the model is wrong.
- Develop a mindset of checking the model before believing in the score.
What is data leakage?
Leakage occurs when the model accidentally sees information that it was not actually allowed to know when predicting, for example, normalizing data before splitting or creating a feature using data after the target time.
Signs of suspected leakage
- The results are too unusually beautiful compared to professional intuition.
- Validation score is very high but actual implementation is poor.
- Some features sound too familiar.
Error analysis
After training the model, see which patterns are most incorrectly predicted, which user groups the errors are concentrated in, and whether any patterns are related to missing data, outliers, or special segments.
Quick check-in process
- Check which features appear only after the target event.
- Check if all preprocessing is in the pipeline.
- View the top worst errors and recurring error groups.
- Confirm whether the data division is correct in time logic or user logic.
Common mistakes
- Just look at the beautiful leaderboard and believe it right away.
- Do not read several dozen lines of actual error data.
- Ignore the time factor in the forecasting problem.
Practice exercises
- Create your own small leakage example and observe the score increase abnormally.
- Go back and fix the pipeline properly.
- Make an error analysis table for 20 wrong prediction samples.
Completion criteria
- Detects at least 3 common types of leakage.
- Get into the habit of looking at real error samples instead of just looking at scores.
- Know why a model with good scores may not be usable.
Practice step by step (advanced)
- Create a data timeline to mark when the feature was created.
- Check if all features violate the prediction time.
- Retrain the model after eliminating suspected leakage features.
- Compare before/after metrics to quantify leakage impact.
- Do error analysis on the top 30 most incorrect predictions.
Artifact should be submitted
- Feature audit table by timeline.
- Report leakage findings and corrective actions.
- Main error group table with improvement suggestions.
Self-test questions
- How is preprocessing-type leakage different from leakage target-type leakage?
- Why can a model that is too beautiful be a dangerous signal?
- How does error analysis help choose directions for improvement?