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Related Questions
- What are some strategies for augmenting the training dataset with synthetic data?
- How can data sampling techniques, such as oversampling the minority class or undersampling the majority class, help improve model generalizability?
- What is the concept of data ensembling, and how can it be used to combine multiple models trained on diverse datasets?
- Can you explain the process of data augmentation, and how it can be applied to images, text, and other types of data?
- How does transfer learning help to prevent overfitting by leveraging pre-trained models on large, diverse datasets?
- What are some techniques for injecting noise or uncertainty into the training data to improve model robustness?
- Can you discuss the role of active learning in selecting the most informative samples from a large dataset to improve model generalizability?
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