Relabelling: meaning, definitions and examples
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relabelling
[ ˌriːˈleɪbəlɪŋ ]
data management
Relabelling refers to the process of changing the label or category assigned to a dataset or a set of observations. This is commonly done to improve the accuracy of classification algorithms or to ensure consistency in the data used for machine learning models.
Synonyms
reclassifying, renaming, updating
Examples of usage
- The team is relabelling the images for better classification accuracy.
- After reviewing the data, she decided relabelling was necessary to fit the new criteria.
- They are relabelling all instances of spam in the dataset.
- During the project, relabelling helped improve the model's performance.
Translations
Translations of the word "relabelling" in other languages:
🇵🇹 reclassificação
🇮🇳 पुनर्नामकरण
🇩🇪 Umlabeln
🇮🇩 penamaan ulang
🇺🇦 переназивання
🇵🇱 przelabelowanie
🇯🇵 再ラベリング
🇫🇷 re-étiquetage
🇪🇸 re-etiquetado
🇹🇷 yeniden etiketleme
🇰🇷 다시 라벨 붙이기
🇸🇦 إعادة تسمية
🇨🇿 přejmenování
🇸🇰 prelabelovanie
🇨🇳 重新标记
🇸🇮 ponovno označevanje
🇮🇸 endurmerking
🇰🇿 қайта атау
🇬🇪 მეორე სახელწოდება
🇦🇿 yenidən adlandırma
🇲🇽 re-etiquetado
Etymology
The term 'relabelling' is derived from the prefix 're-', which means 'again' or 'back', and 'label', which originates from the Old French word 'label' meaning 'a small piece of paper, cloth, or other material that is attached to something to give information about it'. The combination suggests an action of labeling again or modifying the existing labels. As data science and machine learning have evolved, relabelling has become a critical process, especially in the context of supervised learning where the quality of the labels directly affects the model's ability to learn from data. The concept is widely used in various sectors including marketing, healthcare, and IT, as organizations tailor their data processes to meet specific goals and improve accuracy.