What are the differences between accuracy and other metrics in classification?

What are the differences between accuracy and other metrics in classification?

Accuracy measures the proportion of correct predictions, but it can be misleading in imbalanced datasets. Other metrics like precision, recall, and F1-score provide a more nuanced view.

Can you explain precision and recall?

Sure! Precision is the ratio of true positive predictions to the total predicted positives, while recall is the ratio of true positives to the actual positives. They help assess the model's performance in different ways.

When should I use F1-score instead of accuracy?

F1-score is particularly useful when you have imbalanced classes, as it balances precision and recall. It's a better measure when false positives and false negatives have different costs.

What are some common pitfalls when interpreting these metrics?

Common pitfalls include relying solely on accuracy in imbalanced datasets, misunderstanding the trade-offs between precision and recall, and not considering the context of the problem.