Regression models on the How to Make AI Models track. Regression predicts a number. Watch the units and outliers. A few huge targets can dominate squared error. Absolute error is calmer.
This lesson assumes you already worked through Classification versus generation.
The idea in practice
Clip or transform extreme targets on purpose. Report error in the unit users understand.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Regression models',
}
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 model that predicts negative prices because nothing constrained it. 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
Choose squared or absolute error for house prices and say why.
Self-check
- Say Regression models 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.