Ensembles on the How to Make AI Models track. Random forests and gradient boosting average or add many trees. They often win on tabular data. They are slower to explain than one linear model.
This lesson assumes you already worked through Decision trees.
The idea in practice
Compare the ensemble to the linear baseline on the same split. Keep the linear model if the gain is tiny.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Ensembles',
}
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
An ensemble that uses a leaked column and looks unbeatable. 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
Say when you would ship the linear model anyway.
Self-check
- Say Ensembles 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.