What is the difference between ROC curve and Precision-Recall curve?
The ROC curve plots the true positive rate against the false positive rate, while the Precision-Recall curve plots precision against recall.
When should I use the ROC curve instead of the Precision-Recall curve?
Use the ROC curve when you have a balanced dataset, and the Precision-Recall curve is better for imbalanced datasets.
Can you explain what precision and recall mean?
Precision is the ratio of true positives to the sum of true and false positives, while recall is the ratio of true positives to the sum of true positives and false negatives.
How do I interpret the area under the ROC curve?
The area under the ROC curve (AUC) represents the model's ability to distinguish between classes; a value of 1 indicates perfect classification.
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