What are some common evaluation metrics for machine learning models?

What are some common evaluation metrics for machine learning models?

Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.

Can you explain the difference between precision and recall?

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

What is the F1 score and when should I use it?

The F1 score is the harmonic mean of precision and recall, useful when you need a balance between the two.

How do I choose the right metric for my model?

Choosing the right metric depends on your specific problem and what you value more: false positives or false negatives.