Normalized: meaning, definitions and examples
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normalized
[ ˈnɔːr.mə.laɪzd ]
mathematics
Normalized refers to the process of adjusting values measured on different scales to a common scale. This technique is often used in various fields, such as statistics and computing, to allow for fair comparisons between data sets. For instance, in machine learning, normalization is essential for feature scaling so that different features contribute equally to the final model. It can also relate to transforming data distributions to achieve a normal distribution. Overall, normalization enhances the interpretability and accuracy of data analysis.
Synonyms
adjusted, scaled, standardized.
Examples of usage
- Normalized data shows clearer trends.
- We need to apply normalization to the dataset.
- Normalized scores make comparisons easier.
processing
To normalize is to bring a process, value, or system into a standard or regular state. In data processing, normalization involves adjusting values to a common scale without distorting differences in the ranges of values. This is crucial in ensuring that the analysis and interpretations from the data are accurate and reliable. In various scenarios, such as image processing or audio processing, normalization can significantly enhance performance by standardizing inputs.
Synonyms
harmonize, regulate, standardize.
Examples of usage
- Normalize the image size before processing.
- We should normalize the audio files for consistency.
- It is crucial to normalize these values for accurate results.
Translations
Translations of the word "normalized" in other languages:
🇵🇹 normalizado
🇮🇳 मानकीकृत
🇩🇪 normalisiert
🇮🇩 dinormalisasi
🇺🇦 нормалізований
🇵🇱 znormalizowany
🇯🇵 正規化された
🇫🇷 normalisé
🇪🇸 normalizado
🇹🇷 normalize edilmiş
🇰🇷 정규화된
🇸🇦 موحد
🇨🇿 normalizovaný
🇸🇰 normalizovaný
🇨🇳 规范化的
🇸🇮 normaliziran
🇮🇸 staðlaður
🇰🇿 нормаланған
🇬🇪 ნორმალიზებული
🇦🇿 normallaşdırılmış
🇲🇽 normalizado
Etymology
The term 'normalize' originates from the Latin word 'norma', which means a rule or pattern. In mathematics, the concept of normalization began to appear in the late 19th century, where it referred to the process of establishing a standard or norm. The transition into more diverse fields such as statistics and data science occurred throughout the 20th century, reflecting the increasing need to compare data across varying dimensions and measurements. As technology advanced, especially with the rise of computing and data analysis in the 21st century, normalization became a vital process for ensuring that machine learning models and statistical analyses yield reliable results. This evolution traces a path from rudimentary mathematical principles to sophisticated data processing techniques.