Regularization
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
Add a penalty term (such as the absolute value of parameters) to the loss function to limit model complexity and prevent overfitting. It is better to sacrifice a little training accuracy to keep the model simple. Scaffolding role: artificially introduced constraints. When interpreting the world or making plans, if you find the explanation too complex (overfitting), force yourself to eliminate secondary variables by introducing a "regularization term" (Occam's razor). Simple models often generalize better.
SCAFFOLDING EFFECT
Reduce cognitive load
Artificially introduced constraints. When interpreting the world or making plans, if you find the explanation too complex (overfitting), force yourself to eliminate secondary variables by introducing a "regularization term" (Occam's razor). Simple models often generalize better.
Anchor fast decisions
Add a penalty term (L1/L2) to the loss to constrain parameter scale, reduce model complexity, improve generalization, and suppress overfitting.
MINIMUM ACTION
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Regularizationverified
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