What is the difference between accuracy and F1 score?

Can you explain the difference between accuracy and F1 score?

Sure! Accuracy measures the proportion of correct predictions, while F1 score considers both precision and recall.

Why is F1 score often preferred over accuracy?

F1 score is preferred in cases of imbalanced datasets because it provides a better measure of the model's performance on the minority class.

Can you give an example where accuracy might be misleading?

Absolutely! In a dataset with 95% of samples belonging to one class, a model predicting only that class could have 95% accuracy but would fail to identify the minority class.

How do you calculate the F1 score?

The F1 score is calculated using the formula: F1 = 2 * (precision * recall) / (precision + recall). It balances both metrics.