Model Training & Adaptation
Feature engineering
Preparing useful clues from information so an AI can learn patterns more easily.
Example
A team derives days since the last purchase from transaction dates.
Why people use it
It helps turn raw records into clues that are more useful for learning.
What you'll hear
“Calculate days since the last purchase from these dates.”
What this means for you
Validate features on data that reflects real prediction conditions.
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 feature engineering guarantee better performance?
- No. New features can be irrelevant, biased or leak future information.
- Can a useful-looking clue be unavailable in real use?
- Yes. Information recorded only after an event cannot fairly help predict that event beforehand.
- Can it make explanations easier?
- Sometimes. A meaningful derived measurement may be easier to discuss than many raw records.