Match the model to the problem on the How to Make AI Models track. Tables with mixed columns often want linear models or trees. Images want convolutions or pretrained vision models. Text wants tokens and attention, or a simpler bag of words if the task is easy.
This lesson assumes you already worked through A model is a function with parameters.
The idea in practice
Write the data shape first. Choose the smallest family that can represent the decision.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Match the model to the problem',
}
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
One architecture for every problem. 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
Match three data shapes to a model family and justify one rejection.
Self-check
- Say Match the model to the problem 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.