# F-Measure

## Definition of F-Measure

F-Measure: F-Measure is a statistic used in machine learning to measure the effectiveness of a classification model. It is a harmonic mean of precision and recall, both of which are ratios of correct classifications to total number of classifications.

## What is an F-Measure used for?

F-Measure is an important metric used to evaluate the performance of machine learning and data science algorithms, particularly in classification tasks. It is a measure of statistical accuracy that takes into account both precision and recall when assessing a model’s performance. F-measure is a harmonic mean of precision and recall, meaning that it emphasizes both high precision and high recall over low precision or low recall values. In other words, it gives an overall balance of the modelâ€™s ability to accurately classify positive examples as positives and negative examples as negatives. F-measure is also often referred to as the F1 Score.

To calculate the F-measure, one must first calculate the precision and recall values for each class in a multi-class classification problem (or for all classes combined in a binary classification problem). Precision refers to how accurately a model predicts positive classes while recall refers to how many of the actual positives were correctly predicted by the model. The F-measure is then calculated by taking the harmonic mean between precision and recall:

F = 2 x (precision x recall) / (precision + recall)

This metric gives an indication of how well a model performs across all classes, rather than just focusing on single metrics like accuracy or AUC. It also helps provide insight into false positive and false negative rates which can be very useful in understanding why certain models are performing well or poorly on certain datasets. Additionally, since it incorporates both precision and recall into its calculation, it can be used to compare models with different class imbalances (e.g., comparing two models that have very different proportions of positive/negative examples). Finally, because it takes both metrics into account, it is less sensitive to outliers than either precision or recall alone would be which makes it more reliable when comparing different models.