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Related Questions
- What are the definitions of precision, recall, and F1 score, and how are they used to evaluate model performance?
- How can class imbalance affect a model's performance, and what are some strategies for handling it?
- What are some techniques for optimizing a model's precision, recall, and F1 score for imbalanced datasets?
- How can the receiver operating characteristic (ROC) curve be used to evaluate a model's performance on imbalanced datasets?
- What is the relationship between precision, recall, and F1 score, and how can they be used to identify biases in a model's performance?
- How can oversampling the minority class or undersampling the majority class be used to handle class imbalance?
- What are some additional metrics that can be used to evaluate a model's performance on imbalanced datasets, such as the area under the precision-recall curve (AUPRC) and the area under the ROC curve (AUROC)?
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