Interview review on the AI Learning track. Interviewers ask how you would know a model generalized, what leakage looks like, and which metric matches a rare event. They want a procedure.
This lesson assumes you already worked through The project loop.
The idea in practice
Practice: split rule, baseline, metric, and one leakage example from your own words.
A concrete check
goal = {
'track': 'AI Learning',
'lesson': 'Interview review',
}
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
Answering with a library name and no 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
Answer out loud in four sentences. Note what you skipped.
Self-check
- Say Interview review 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.