Regularizing: meaning, definitions and examples
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regularizing
[ ˈrɛɡjʊləraɪzɪŋ ]
mathematics, statistics
Regularizing is a technique used to prevent overfitting in models by adding a penalty for larger coefficients during the training process. It helps ensure that the model generalizes well to unseen data. Common regularization techniques include L1 (lasso) and L2 (ridge) regularization.
Synonyms
constraining, normalizing, stabilizing.
Examples of usage
- The model's performance improved significantly after regularizing the coefficients.
- Regularizing helps to stabilize the learning process in machine learning.
- We used a regularizing term to enhance the training of our neural network.
Translations
Translations of the word "regularizing" in other languages:
🇵🇹 regularização
🇮🇳 नियमित करना
🇩🇪 Regulierung
🇮🇩 penyelarasan
🇺🇦 регулювання
🇵🇱 uregulowanie
🇯🇵 規則化
🇫🇷 régularisation
🇪🇸 regularización
🇹🇷 düzenleme
🇰🇷 정규화
🇸🇦 تنظيم
🇨🇿 regulace
🇸🇰 regulácia
🇨🇳 规范化
🇸🇮 urejanje
🇮🇸 reglugerandi
🇰🇿 реттеу
🇬🇪 რეგულირება
🇦🇿 tənzimləmə
🇲🇽 regularización
Etymology
The term 'regularizing' comes from the root word 'regular', which originates from the Latin word 'regularis', meaning 'arranged according to rule'. In the context of mathematics and statistics, the concept of regularization has evolved to signify methods that are employed to modify an estimation process. Over time, as data science and machine learning have advanced, regularization techniques have become crucial for model training to control complexity and achieve better predictive performance. The introduction of regularization methods can be traced back to the late 20th century, where they were developed to address the challenges posed by high-dimensional data.