Leakage on the AI Learning track. Leakage is information in the features that would not be available at decision time, or that contains the target. The validation score becomes a lie. Production then looks mysteriously worse.
This lesson assumes you already worked through Data quality before models.
The idea in practice
Ask, for every column: do I know this before the decision? Remove aggregates that include the row's own future. Split before fitting any scaler that uses statistics.
A concrete check
goal = {
'track': 'AI Learning',
'lesson': 'Leakage',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
Fitting a normalizer on the full dataset before the split. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
Give one example of leakage in a churn model.
Self-check
- Say Leakage in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.