Аугментация данных — это процесс расширения training dataset путем создания измененных, synthetic или transformed versions существующих данных. Она обычно используется для повышения data diversity, снижения overfitting и улучшения model robustness, когда сбор дополнительных real-world examples непрактичен.
Data augmentation in data analysis are techniques used to increase the amount of data. It helps reduce overfitting when training a learning algorithm.
Artificially boosting the range and number of training examples by transforming existing examples to create additional examples. For example, suppose images are one of your features, but your dataset doesn't contain enough image examples for the model to learn useful associations. Ideally, you'd add enough labeled images to your dataset to enable your model to train properly. If that's not possible, data augmentation can rotate, stretch, and reflect each image to produce many variants of the original picture, possibly yielding enough labeled data to enable excellent training.
Artificially boosting the range and number of training examples by transforming existing examples to create additional examples. For example, suppose images are one of your features, but your dataset doesn't contain enough image examples for the model to learn useful associations. Ideally, you'd add enough labeled images to your dataset to enable your model to train properly. If that's not possible, data augmentation can rotate, stretch, and reflect each image to produce many variants of the original picture, possibly yielding enough labeled data to enable excellent training.