F1-Score
An evaluation metric for classification models that calculates the harmonic mean of precision and recall, providing a balanced measure for imbalanced datasets.
Think of It Like This
Like judging a fisherman not just by how many fish they caught, but also by making sure they didn't catch too much garbage in the process of fishing.
The F1-score is particularly useful when the class distribution is highly uneven, such as in fraud detection where negative examples vastly outnumber positive ones. Unlike simple accuracy, which can be misleadingly high in these scenarios, the F1-score requires both false positives and false negatives to be low. It reaches its best value at 1 and worst at 0.