What are common evaluation metrics for ML models?

What are some common evaluation metrics used for machine learning models?

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

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?

F1 score is preferred when you need a balance between precision and recall, especially in imbalanced datasets.

What is AUC-ROC and why is it important?

AUC-ROC represents the area under the receiver operating characteristic curve, indicating the model's ability to distinguish between classes.