What are the applications of the F1 score in machine learning?

What are the applications of the F1 score in machine learning?

The F1 score is primarily used in classification problems to evaluate a model's accuracy, especially with imbalanced datasets.

Can you explain why the F1 score is important?

Certainly! The F1 score balances precision and recall, making it crucial when false positives and false negatives carry different costs.

In which scenarios should I prefer the F1 score over accuracy?

You should prefer the F1 score over accuracy in cases where the classes are imbalanced, as accuracy can be misleading.

Are there specific industries that rely heavily on the F1 score?

Yes, industries like healthcare, finance, and cybersecurity often rely on the F1 score to ensure reliable predictions.

How can I calculate the F1 score for my model?

You can calculate the F1 score using the formula: F1 = 2 * (precision * recall) / (precision + recall).