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Regularized: meaning, definitions and examples

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regularized

 

[ ˈrɛɡjʊləˌraɪzd ]

Adjective / Verb
Context #1 | Adjective

mathematics

Regularized refers to the process of adding constraints or penalties to a model to prevent overfitting and enhance its generalization capabilities. This technique is commonly used in various forms of regression and machine learning.

Synonyms

constrained, controlled, suppressed.

Examples of usage

  • The regularized model performed better on unseen data.
  • We applied L2 regularization to the regression equation.
  • Regularized methods help stabilize the solution in complex datasets.
Context #2 | Verb

mathematics

To regularize means to apply a regularization technique to a mathematical model or algorithm. This often involves modifying the objective function to incorporate a penalty for certain parameter values.

Synonyms

constrain, normalize, standardize.

Examples of usage

  • We need to regularize the algorithm to avoid overfitting.
  • The team decided to regularize the data to improve accuracy.
  • Regularizing the neural network made it more robust.

Translations

Translations of the word "regularized" in other languages:

🇵🇹 regularizado

🇮🇳 नियमित

🇩🇪 reguliert

🇮🇩 teratur

🇺🇦 регуляризований

🇵🇱 uregulowany

🇯🇵 規則化された

🇫🇷 régularisé

🇪🇸 regularizado

🇹🇷 düzenlenmiş

🇰🇷 정규화된

🇸🇦 منتظم

🇨🇿 regulovaný

🇸🇰 regulovaný

🇨🇳 规范化的

🇸🇮 urejen

🇮🇸 reglugeruð

🇰🇿 реттелген

🇬🇪 რეგულარიზებული

🇦🇿 tənzimlənmiş

🇲🇽 regularizado

Etymology

The term 'regularized' derives from the word 'regularize', which came into use in the early 20th century. It is formed by combining 'regular'—from the Latin 'regula', meaning rule or straight edge—with the suffix '-ize', which indicates the process of making. In mathematics and statistics, the concept of regularization became increasingly popular as data science and machine learning grew in relevance from the 1990s onward. Regularization techniques such as Lasso and Ridge regression emerged to combat issues related to overfitting, thereby enhancing model robustness and performance.

Word Frequency Rank

At rank #27,889, this word represents specialized academic or technical vocabulary. It's less frequently encountered but may be valuable in specific contexts.