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데이터 첨가(data augmentation) 또는 데이터 증강은 주로 기계 학습을 위해 새로운 데이터를 첨가하거나, 수를 늘리는 기법이다. 과대표집(oversampling)과 관련이 있다. 정칙화 기능을 하여 기계 학습 모델을 훈련할 때 과적합을 줄이는 데에 도움이 된다.
, Data augmentation in data analysis are tec … Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model. It is closely related to oversampling in data analysis. related to oversampling in data analysis.
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| rdfs:comment |
Data augmentation in data analysis are tec … Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model. It is closely related to oversampling in data analysis. related to oversampling in data analysis.
, 데이터 첨가(data augmentation) 또는 데이터 증강은 주로 기계 학습을 위해 새로운 데이터를 첨가하거나, 수를 늘리는 기법이다. 과대표집(oversampling)과 관련이 있다. 정칙화 기능을 하여 기계 학습 모델을 훈련할 때 과적합을 줄이는 데에 도움이 된다.
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데이터 첨가
, Data augmentation
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