Regularizing Meaning: Definition, Examples, and Translations
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regularizing
[หrษษกjสlษraษชzษชล ]
Definition
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
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Interesting Facts
Mathematics
- In mathematics, regularizing techniques help solve ill-posed problems, improving stability and accuracy.
- Regularization methods, like ridge regression, add penalties to prevent model overfitting in statistics.
- The concept plays a key role in machine learning to ensure models generalize well to new data.
Sociology
- In social contexts, regularizing can refer to standardizing laws or practices within communities.
- The process can help in reducing inequality by ensuring everyone follows the same rules.
- Regularizing informal economies allows for better governance and social integration.
Business
- In business, regularizing processes can lead to increased efficiency and better compliance with regulations.
- Standard operating procedures (SOPs) are a form of regularization to ensure consistency across operations.
- Regularizing contracts ensures all parties understand their obligations, reducing disputes.
Psychology
- Cognitive regularization helps individuals understand complex information by creating patterns.
- In therapy, regularizing routines can improve mental health by providing structure and predictability.
- Social regularization can enhance group dynamics by creating shared norms and behaviors.
Origin of 'regularizing'
Main points about word origin
- The word comes from the Latin 'regularis', meaning 'according to rule' or 'normal'.
- It first appeared in English usage during the late 19th century as a way to describe making processes orderly.
- The suffix '-izing' is used to denote the process of doing something, hinting at transformation.
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.