Ranking on the How to Make AI Models track. Ranking orders items for a user. Accuracy of a click model is not the same as quality of the list. Measure the top of the list.
This lesson assumes you already worked through Regression models.
The idea in practice
Hold out users or time. Do not leak the click you are predicting into the features.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Ranking',
}
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
Training on the items you already showed and calling it unbiased. 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
Name a top-k metric you would report.
Self-check
- Say Ranking 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.