F1 is a key metric in artificial intelligence and machine learning that measures a model’s accuracy by combining two important aspects: precision and recall. It is particularly valuable for evaluating classification tasks, especially when dealing with imbalanced datasets where one class is much more frequent than the other. Rather than just looking at how many predictions are correct overall, the F1 score provides a balance between being precise (not labeling negatives as positives) and being sensitive (catching as many real positives as possible).
The F1 score is the harmonic mean of precision and recall. Precision tells you how many of the items the model labeled as positive are actually positive, while recall indicates how many actual positive items were correctly identified by the model. The harmonic mean is used instead of the regular average because it punishes extreme values more, ensuring that both precision and recall have to be reasonably high for the F1 score to be high. The formula looks like this:
F1 = 2 × (precision × recall) / (precision + recall)
Suppose you’re building a machine learning model to detect spam emails. If your model is very cautious, it might rarely label an email as spam (high precision, low recall), missing many actual spam emails. Alternatively, a model could mark almost everything as spam (high recall, low precision), but then you’d get lots of false alarms. A good F1 score means the model does well on both fronts.
The F1 score ranges from 0 to 1, with 1 being perfect precision and recall and 0 being the worst. In many real-world AI applications, especially those involving rare events (like fraud detection or disease screening), the F1 score is a more informative metric than simple accuracy. This is because accuracy can be misleading when the majority class dominates; for example, if 99% of emails are not spam, a model that always predicts “not spam” would have 99% accuracy but 0 F1 score for the spam class.
It’s also common to report the F1 score for each class in multiclass classification problems. In such cases, metrics like macro-F1 and micro-F1 aggregate the F1 scores across classes in different ways. Macro-F1 averages scores for each class equally, while micro-F1 gives more weight to classes with more samples.
Overall, F1 is a practical, widely used measure for understanding how well a model balances the tradeoffs between catching positives and avoiding false alarms. It’s a go-to metric for researchers and practitioners when evaluating and comparing models, especially when data is skewed or the cost of false positives and false negatives is not the same.
