F1 Score
The F1 Score is a statistical measure used to evaluate the performance of a classification model, particularly in scenarios where the class distribution is imbalanced. It serves as a balance between precision and recall, providing a single metric that captures both false positives and false negatives.
Precision is the ratio of true positive predictions to the total predicted positives, indicating how many of the predicted positive cases were actually correct. Recall, on the other hand, is the ratio of true positive predictions to the total actual positives, measuring how many of the actual positive cases were correctly identified by the model.
The F1 Score is calculated using the formula:
F1 = 2 * (Precision * Recall) / (Precision + Recall)
This formula highlights how the F1 Score is the harmonic mean of precision and recall, which means it tends to be lower when either precision or recall is low. This property makes it particularly useful in situations where one metric cannot be sacrificed for the other, ensuring that both the accuracy of positive predictions and the model’s ability to capture actual positive instances are taken into account.
The F1 Score ranges from 0 to 1, where 1 indicates perfect precision and recall (all positive predictions are correct, and all positive cases are identified), while a score of 0 indicates the worst performance. It is widely used in fields such as natural language processing, medical diagnosis, and fraud detection, where the cost of false positives and false negatives can significantly impact outcomes.