What is the F1 Score and why is it important?
The F1 Score is a metric that combines precision and recall to evaluate a model's performance, especially in classification tasks.
How is the F1 Score calculated?
The F1 Score is calculated using the formula: F1 = 2 * (precision * recall) / (precision + recall). It balances both metrics.
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 a model's performance on minority classes.
Can you give an example of F1 Score in a real-world scenario?
Sure! In medical diagnosis, using the F1 Score helps ensure that both false positives and false negatives are minimized, which is crucial for patient safety.
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