A complete learning report on the AI Learning track. The capstone is a short report: question, split, baseline, your model, validation metric, three errors, and what you would not claim.
This lesson assumes you already worked through Interview review.
The idea in practice
Two pages are enough. Include the curve or the reason you stopped. State a limit of the data.
A concrete check
goal = {
'track': 'AI Learning',
'lesson': 'A complete learning report',
}
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
A screenshot of accuracy with no split description. 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
Draft the report headings and fill the split and the baseline even if the model is simple.
Self-check
- Say A complete learning report 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.