What are the key metrics for evaluating classification models?

What are the key metrics for evaluating classification models?

The key metrics include accuracy, precision, recall, F1 score, and AUC-ROC. Would you like to know more about any specific metric?

Can you explain precision and recall?

Sure! Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances. They are often used together to evaluate model performance.

What is the F1 score?

The F1 score is the harmonic mean of precision and recall, providing a balance between the two. It's especially useful when you have an imbalanced dataset.

How do I choose the right metric for my model?

Choosing the right metric depends on your specific use case. For example, if false positives are costly, prioritize precision. If false negatives are critical, focus on recall.