Regularization
Definition
Regularization limits model behavior for better transferability to new data.
Methods that limit model complexity or parameter behavior to improve generalization or promote desired structures.
Simply explained
Additional constraints are imposed during learning to prevent the model from overfitting to its training data.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
An L2 term suppresses very large weights in a regression model.
Limits and pitfalls
- Equate regularization with guaranteed better test performance or tune its strength on test data.
Origin & history
Regularization was developed in statistics and inverse problems; L1, L2, and Dropout are common variants.
Use in mid-sized companies
- Possible example: An L2 term suppresses very large weights in a regression model.
Frequently asked questions
What does regularization mean?
Methods that limit model complexity or parameter behavior to improve generalization or promote desired structures. What mistakes should be avoided? Equating regularization with guaranteed better test performance or tuning its strength on test data.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: