Selecting the model to keep on the How to Train Models track. Selection uses validation, the baseline, cost, and latency. The lowest loss is not automatic. A simpler model that is almost as good is often the one to ship.
This lesson assumes you already worked through Calibration.
The idea in practice
Write the selection rule before you see the table. Include a latency budget.
A concrete check
goal = {
'track': 'How to Train Models',
'lesson': 'Selecting the model to keep',
}
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
Picking the largest model because it won by a tiny, noisy margin. 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
Write a selection rule with one quality bar and one cost bar.
Self-check
- Say Selecting the model to keep 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.