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Related Questions
- Can data augmentation techniques such as rotation, flipping, and color jittering help to increase the diversity of training data and reduce the impact of missing values?
- How does data augmentation affect the model's ability to generalize to unseen data and handle missing values?
- In what ways can data augmentation be used to create synthetic data that can help fill in missing values in a dataset?
- Can data augmentation be used to improve the robustness of a deep learning model to missing values by reducing overfitting?
- How does the type and amount of data augmentation used impact the model's performance on data with missing values?
- Can data augmentation be combined with other techniques such as imputation or interpolation to further improve the model's handling of missing values?
- How does the use of data augmentation affect the model's interpretability when dealing with missing values?
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