feature reduction
[ˈfiːtʃər rɪˈdʌkʃən]
nounpl: feature reductions
redução de dimensionalidade
1. A machine learning and data analysis technique that reduces the number of input variables (features) in a dataset while preserving relevant information
Feature reduction improved the model's performance by eliminating redundant variables.
A redução de dimensionalidade melhorou o desempenho do modelo eliminando variáveis redundantes.
2. The process of selecting or transforming features to decrease computational complexity and improve model interpretability
Principal Component Analysis is a common method for feature reduction.
Análise de Componentes Principais é um método comum para redução de dimensionalidade.
This is primarily a technical term used in machine learning and data science communities in both Brazil and the USA. It reflects the growing importance of AI and data science education and industry. In Brazil, 'redução de dimensionalidade' is the preferred academic translation, while in Portugal, 'redução de características' is more common. The concept is essential in modern data analysis and is taught in universities and professional courses globally.
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