In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance.

2020年8月1日 · F-measure provides a way to express both concerns with a single score. Once precision and recall have been calculated for a binary or multiclass classification problem, the two scores can be combined into the calculation of the F-Measure.

The F score, also called the F1 score or F measure, is a measure of a test’s accuracy.

The F-measure of the system is defined as the weighted harmonic mean of its precision and recall, that is, \(F = {1\over \alpha {1\over P}+(1-\alpha ) {1\over R}}\), where the weight α ∈ [0,1]. The balanced F-measure, commonly denoted as F 1 or just F, equally weighs precision and recall, which means α = 1∕2.

2025年1月8日 · The F-measure, also known as the F1 score, uses the harmonic mean rather than the arithmetic mean to combine precision and recall into a single measure. This choice is intentional as the harmonic mean penalizes extreme values more heavily than the …

F-score, a metric for evaluating the accuracy of a binary classification model. It combines the precision and recall of an algorithm into one metric. A binary classification model classifies items as one of two values—for example, “yes” or “no.” Precision is …

Often the metrics are combined into a single performance measure called an F-score, using the following formula: F-score = 2 (precision × recall) / (precision + recall). Like precision and recall, F-scores range from 0 (indicating a complete lack of precision, recall, or both measures) to 1 (representing both perfect precision and perfect recall).

2023年11月17日 · Learn how the F-measure, a popular evaluation metric in machine learning, can help measure the accuracy and balance of classification models. Understand its significance and application in optimizing model performance.

2023年6月28日 · We provide a history of the F-measure and its use in computational disciplines, describe its properties, and discuss criticism about the F-Measure. We conclude with alternatives to the F-measure, and recommendations of how to use it effectively.

2021年3月15日 · To ease this concern, we describe a simple transformation of the F-measure, which we call $$F^*$$ (F-star), which has an immediate practical interpretation. The F-measure, also known as the F1-score, is widely used to assess the performance of classification algorithms.