Discretization: meaning, definitions and examples
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discretization
[ dɪˌskrəʊtəˈzeɪʃən ]
data analysis
Discretization is the process of transforming continuous data or variables into discrete counterparts. This is often done in order to simplify the dataset, making it more manageable for analysis and interpretation. It can involve defining ranges for continuous values or categorizing data into distinct groups.
Synonyms
categorization, classification, segmentation
Examples of usage
- The discretization of continuous variables helps in machine learning models.
- Discretization can lead to loss of information if not done carefully.
- In statistics, discretization is used to simplify complex data.
- The discretization step improves the performance of algorithms.
Translations
Translations of the word "discretization" in other languages:
🇵🇹 discretização
🇮🇳 विभाजन (discretization)
🇩🇪 Diskretisierung
🇮🇩 diskretisasi
🇺🇦 дискретизація
🇵🇱 dyskretizacja
🇯🇵 離散化 (りさんか)
🇫🇷 discrétisation
🇪🇸 discretización
🇹🇷 diskretizasyon
🇰🇷 불연속화
🇸🇦 التفريق (discretization)
🇨🇿 diskretizace
🇸🇰 diskretizácia
🇨🇳 离散化 (lísànhuà)
🇸🇮 diskretizacija
🇮🇸 diskretisering
🇰🇿 дискретизация
🇬🇪 დისკრეტიზაცია
🇦🇿 diskretizasiya
🇲🇽 discretización
Etymology
The term 'discretization' has its roots in the word 'discrete', which comes from the Latin 'discretus', meaning 'separate, distinct, or set apart'. The first known use of 'discrete' dates back to the 14th century. As mathematics and data analysis evolved, the concept of discretizing continuous variables emerged, particularly in the fields of statistics and computer science. The process became especially significant in the late 20th century with the rise of machine learning and data mining, where handling vast amounts of data in a manageable form was crucial. Discretization is widely used in various domains, including economics, natural sciences, and artificial intelligence, to facilitate analysis and model building.