Linear and logistic models on the How to Make AI Models track. A linear model scores a weighted sum of features. Logistic regression squashes that score into a probability. They are strong baselines and they are debuggable.
This lesson assumes you already worked through Match the model to the problem.
The idea in practice
Standardize numeric features. One-hot categories. Read the weights to see direction.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Linear and logistic 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
Ignoring coefficients and treating the linear model as a black box you skip past. 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
Explain what a positive weight means for one feature.
Self-check
- Say Linear and logistic 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.