Normalizing: meaning, definitions and examples
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normalizing
[ ˈnɔːr.mə.laɪ.zɪŋ ]
data processing
Normalizing refers to the process of adjusting values in a dataset to a common scale. This is done to eliminate units of measurement and ensure that all values are treated equally, which can facilitate comparisons and improve the performance of machine learning algorithms. It can also involve adjusting data distributions to fit a desired range or distribution pattern. Normalization is critical in various fields such as statistics, data analysis, and machine learning.
Synonyms
adjusting, equalizing, scaling, standardizing.
Examples of usage
- Normalizing the data improved the model's accuracy.
- The first step in preprocessing was normalizing the input values.
- Normalizing ensures that features have equal weight.
- He focused on normalizing the results for better analysis.
Translations
Translations of the word "normalizing" in other languages:
🇵🇹 normalização
🇮🇳 सामान्यीकरण
🇩🇪 Normalisierung
🇮🇩 normalisasi
🇺🇦 нормалізація
🇵🇱 normalizacja
🇯🇵 正規化
🇫🇷 normalisation
🇪🇸 normalización
🇹🇷 normalizasyon
🇰🇷 정규화
🇸🇦 تطبيع
🇨🇿 normalizace
🇸🇰 normalizácia
🇨🇳 标准化
🇸🇮 normalizacija
🇮🇸 normalisering
🇰🇿 нормализация
🇬🇪 ნორმალიზაცია
🇦🇿 normalizasiya
🇲🇽 normalización
Etymology
The term 'normalize' originates from the root word 'normal', which derives from the Latin 'norma', meaning 'a rule or pattern'. In the context of mathematics and statistics, the usage of 'normalize' began to surface in the mid-20th century as data analysis techniques evolved. Initially used in geometry to refer to the process of producing a normal form, it quickly found its way into other domains, particularly in data science. The concept gained momentum with the rise of computer technology and the increasing need for data accuracy and consistency. Today, normalizing remains a fundamental aspect of data preparation and plays a crucial role in fields like machine learning and artificial intelligence.