Decision trees on the How to Make AI Models track. A tree splits features with thresholds. It handles nonlinearities and mixed types. A single deep tree overfits. Limit depth.
This lesson assumes you already worked through Linear and logistic models.
The idea in practice
Set max depth and minimum samples per leaf. Plot feature importances only as a hint, not as proof.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Decision trees',
}
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 depth-unlimited tree with perfect training accuracy. 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
Name two hyperparameters that limit a tree's memorization.
Self-check
- Say Decision trees 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.