Model Training & Adaptation
L2 regularization
A training rule that penalizes larger learned settings more strongly, usually shrinking them rather than removing them.
Example
Training discourages an AI from giving extremely large influence to individual clues.
Why people use it
It discourages a predictor from relying too heavily on very large learned values.
What you'll hear
“Keep individual influences from becoming too large.”
What this means for you
Compare settings using examples kept separate from learning.
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 this penalty add new teaching examples?
- No. It changes how learning is guided rather than supplying additional observations.
- Can a stronger penalty make predictions too simple?
- Yes. It can suppress useful relationships as well as unhelpful ones.
- Does the same penalty suit every collection?
- No. The information and prediction task affect which setting works well.