Model Training & Adaptation
Unsupervised learning
Teaching AI to find patterns in information without giving it the correct answers first.
Example
A system groups customers by similar shopping behavior.
Why people use it
It can reveal patterns when nobody has supplied the right answers in advance.
What you'll hear
“Let's see which groups appear without naming them first.”
What this means for you
Ask what the discovered groups represent before acting on them.
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
- Do the groups come with meaningful names?
- Not necessarily. People must interpret whether the patterns are useful.
- Can two runs find different groups?
- Yes. Different starting choices or methods can divide the same information in different ways.
- Can it find unusual cases too?
- Yes. Some methods spot cases that look different from the rest, which people can then investigate.