regularization

Regularization is the process of making something more regular, stable, or consistent. In everyday technical use, it often means adding rules, limits, or structure so that a system does not become too messy or extreme. In machine learning, this usually means adding a penalty or constraint so a model stays simpler and works better on new data instead of just memorizing the training data. The idea is to guide the model toward the main pattern, not every tiny detail.

Regularization is the act of making something conform more closely to a rule, pattern, or stable form. The word is used in several fields, but the central idea stays the same: you reduce disorder, looseness, or extreme behavior by adding structure.

In machine learning, regularization is a method for preventing overfitting, which happens when a model learns the training data too closely, including noise or accidental patterns. A regularized model is pushed to stay simpler, so it has a better chance of performing well on new examples. This is often done by adding a penalty term to the loss function. Common forms include L1 regularization, which can push some coefficients to zero, and L2 regularization, which shrinks coefficients without usually removing them entirely. In this setting, the word often suggests balance: not too simple, not too complex.

Outside machine learning, regularization can also mean making a process more orderly or officially consistent. In mathematics, it may refer to a method for giving a sensible, stable result to a problem that is otherwise badly behaved, such as a divergent expression. In legal or administrative contexts, it can mean bringing a status, procedure, or document into proper form under the rules. Across these uses, the word points to the same broad idea: making something more controlled, standard, and workable.