The F-score, also known as F1 score or F-measure, is a statistical metric widely used in machine learning and data analysis. It’s a measure that helps us evaluate the performance of a binary classification system (a system that categorizes data points into one of two groups). This measure conveys the balance between precision (how many of the items identified were relevant) and recall (how many of the relevant items were identified).
The F1 score is calculated as the harmonic mean of precision and recall, providing a single quality figure that makes it easier to compare the performance of various models. The F1 score ranges between 0 and 1 — a score of 1 indicates perfect precision and recall, meaning all relevant items were identified and all identified items were relevant. On the other hand, a score of 0 indicates that either the model has zero recall (it didn’t identify any relevant item) or zero precision (none of the items it identified was relevant).
The essence of the F1 score is that it provides a balance between precision and recall. It is particularly useful when dealing with imbalanced datasets (datasets where one class significantly outnumbers the other) where a standard accuracy measure could be misleading. By using the F1 score, one can evaluate a model’s performance in terms of its ability to deliver both precision and recall.