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Related Questions
- How can low-quality synthetic negative examples impact the accuracy of a machine learning model?
- What are the potential biases that can arise from using low-quality synthetic negative examples?
- Can low-quality synthetic negative examples lead to overfitting or underfitting in a model?
- How does the use of low-quality synthetic negative examples affect the model's generalizability?
- What are some strategies to identify and mitigate the effects of low-quality synthetic negative examples?
- Can low-quality synthetic negative examples lead to an increase in false positives or false negatives?
- How can data augmentation techniques be used to improve the quality of synthetic negative examples?
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