CHAPTER 02 / 06 · TRAIN
Train. Test. Explain.
Machine learning Applied AI Engineering
A useful model starts with a baseline, separates learning from evaluation and makes mistakes easy to inspect.
Does the model improve on a simple rule outside its training examples?
A basic rule sets the bar. More complexity only helps when the evidence improves.
02 / THE PROCESS
From idea
to evidence.
Three connected moves explain the subject and the work that makes it reviewable.
An evaluation report that includes failure cases.
- 01 / TRAIN
Start with a baseline
Define a simple comparison before training so improvement has a meaningful reference.
WHAT YOU CAN SHOWBaseline result - 02 / VALIDATE
Protect the validation split
Prepare features without letting information from held-out examples leak into training.
WHAT YOU CAN SHOWDocumented split and features - 03 / COMPARE
Review what fails
Look beyond one score to false positives, false negatives and cases near the boundary.
WHAT YOU CAN SHOWEvaluation with failure cases
THE PRACTICE
Make the work
inspectable.
The example is a way to explore the idea. Real project evidence should include what was tried, what was observed and what remains uncertain.
KEEP IN VIEW
What the visual
doesn’t prove.
The visual uses illustrative data. A smooth boundary alone does not prove that a model generalises.
ROLE CONNECTIONS
These directions use ideas from this subject within the Applied AI Engineering learning path.