Artificial Intelligence & Machine Learning
F1-Score
Definition
The F1-score is a metric used in classification that combines precision and recall into a single score. It is the harmonic mean of precision and recall.
Why It Matters
The F1-score provides a way to find a balance between precision and recall, especially when you are dealing with an imbalanced dataset where one class is much more frequent than the other.
Contextual Example
If a model has high precision but low recall, or vice versa, the F1-score will be low. A high F1-score indicates that the model has both good precision and good recall.
Common Misunderstandings
- F1-Score = 2 * (Precision * Recall) / (Precision + Recall).
- It is a more robust metric than accuracy for imbalanced classification problems.