Model Training & Adaptation
Train-test split
Keeping some examples separate from AI learning so they can provide an independent check afterward.
Example
A team teaches AI with one collection and checks it with another.
Why people use it
It gives AI a fairer check on examples kept apart from its learning.
What you'll hear
“Keep these records aside until the final check.”
What this means for you
Keep closely related or duplicate examples from making the final check artificially easy.
Can you control it?
Developer-only
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Does any random split create a fair test?
- No. Related records, duplicates or time dependence can cross the boundary.
- Why can records from the same person cause trouble?
- Similar information on both sides can make the test easier than predicting for a new person.
- Should future records usually be used to predict the past?
- No. A time-based task should reflect what information would actually have been available then.