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. Each serves a different purpose.

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 use F1 score instead of accuracy?

Use F1 score when you have imbalanced classes, as it considers both precision and recall, providing a better measure.

What is ROC-AUC and why is it important?

ROC-AUC measures the model's ability to distinguish between classes. A higher AUC indicates better model performance.