feature augmentation
[/ˈfiːtʃər ɔːɡˈmɛntəʃən/]
nounpl: feature augmentations
aumento de características
1. A machine learning technique that artificially expands or enhances the feature set of a dataset by creating new features from existing ones, or by applying transformations to increase data variability and model robustness
Feature augmentation techniques such as polynomial expansion and interaction terms can improve model performance on unseen data.
Técnicas de aumento de características como expansão polinomial e termos de interação podem melhorar o desempenho do modelo em dados não vistos.
2. In data preprocessing, the process of generating additional training examples through transformations like rotation, scaling, or noise injection to increase dataset size and diversity
Image feature augmentation through rotation and cropping helped the neural network generalize better.
O aumento de características de imagem através de rotação e corte ajudou a rede neural a generalizar melhor.
Feature augmentation is a critical concept in machine learning widely adopted in both Brazilian and American tech communities. In Brazil, the term is frequently used in academic research at institutions like USP and UFRJ, as well as in tech companies in São Paulo's startup ecosystem. The technique is essential for improving model robustness and addressing data scarcity issues, making it a foundational concept in modern AI development across both countries.
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