What are model evaluation metrics in machine learning?

What are model evaluation metrics in machine learning?

Model evaluation metrics are standards used to assess the performance of machine learning models. They help determine how well a model is performing.

Can you give me some examples of these metrics?

Sure! Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC. Each serves a different purpose depending on the problem.

How do I choose the right metric for my model?

Choosing the right metric depends on your specific use case. For instance, if you have imbalanced classes, precision and recall might be more important than accuracy.

What is the difference between precision and recall?

Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances. They often trade off against each other.