Labels and who makes them on the AI Learning track. A label is the target you train against. Someone defines it. If two labelers would disagree, the model will learn that noise. Write the labeling rule before you label a thousand rows.
This lesson assumes you already worked through Data is the experience.
The idea in practice
Give labelers examples of yes, no, and unsure. Measure agreement on a shared sample. Send unsure to a rule or a second person.
A concrete check
goal = {
'track': 'AI Learning',
'lesson': 'Labels and who makes them',
}
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
Changing the meaning of the label halfway through the file splits the task in two without saying so. 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 labeling guide for 'is this review about shipping' with two positive and two negative examples.
Self-check
- Say Labels and who makes them 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.