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Related Questions
- What are the key differences between precision and recall in machine learning, and how do they impact model performance?
- Can you explain the concept of the F1 score and how it can be used to balance precision and recall?
- What are some common techniques for adjusting the threshold of a classification model to improve both precision and recall?
- How can you use the ROC-AUC score to evaluate the trade-off between precision and recall in a binary classification model?
- What is the relationship between precision and recall in the context of a precision-recall curve, and how can it be used to optimize model performance?
- Can you provide examples of how to implement techniques such as cost-sensitive learning or weighted loss functions to balance precision and recall in a machine learning model?
- How can you use techniques such as oversampling the minority class or undersampling the majority class to balance the class distribution and improve both precision and recall?
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