Error analysis on the AI Learning track. Error analysis is reading the cases you got wrong and grouping them. The groups tell you whether to fix labels, add data, or change features. A single accuracy number does not.
This lesson assumes you already worked through Baselines.
The idea in practice
Sample 50 errors. Tag each with a reason. Fix the largest tag first. Repeat. Do not tune randomly.
A concrete check
goal = {
'track': 'AI Learning',
'lesson': 'Error analysis',
}
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
Staring at a confusion matrix without opening a single wrong row. 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 five error tags you might see in an email spam model.
Self-check
- Say Error analysis 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.