What is the F1 Score in machine learning?

What is the F1 Score in machine learning?

The F1 Score is a metric that combines precision and recall to evaluate a model's performance.

How is the F1 Score calculated?

It's calculated using the formula: F1 = 2 * (precision * recall) / (precision + recall).

When should I use the F1 Score instead of accuracy?

Use the F1 Score when you have imbalanced classes, as it provides a better measure of the model's performance.

Can you give me an example of F1 Score in a classification problem?

Sure! In a binary classification problem, if you have 70 true positives, 10 false positives, and 20 false negatives, the F1 Score would be calculated based on those values.