What metrics should I use for evaluation?

What metrics should I use to evaluate my machine learning model?

It depends on your specific use case, but common metrics include accuracy, precision, recall, and F1 score.

Can you explain the difference between precision and recall?

Sure! Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances.

When should I prioritize recall over precision?

You should prioritize recall when the cost of missing a positive instance is high, like in medical diagnoses.

What about using AUC-ROC as a metric?

AUC-ROC is great for evaluating the trade-off between true positive and false positive rates, especially in binary classification.