Experiment logs on the AI Learning track. An experiment log records data version, code version, hyperparameters, and metrics. Without it you cannot reproduce a good run or explain a bad one.
This lesson assumes you already worked through Leakage.
The idea in practice
One row per run. Store the git commit and the data snapshot id. Do not overwrite the previous best without a note.
A concrete check
goal = {
'track': 'AI Learning',
'lesson': 'Experiment logs',
}
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 notebook you edited after the score, with no record of what changed. 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
List the six fields every training run must log.
Self-check
- Say Experiment logs 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.