What are the different types of evaluation metrics in machine learning?

What are the different types of evaluation metrics in machine learning?

In machine learning, evaluation metrics help assess the performance of models. Common types include accuracy, precision, recall, F1 score, and ROC-AUC.

Can you explain accuracy and how it's calculated?

Accuracy is the ratio of correctly predicted instances to the total instances. It's calculated as (True Positives + True Negatives) / Total Instances.

What about precision and recall? How do they differ?

Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances. They are crucial for imbalanced datasets.

When should I use F1 score instead of accuracy?

F1 score is useful when you need a balance between precision and recall, especially in cases of class imbalance where accuracy might be misleading.