What is fscoring?

F-scoring is a statistical measure used to evaluate the performance of a binary classification model. It is often used in machine learning and data mining to determine the accuracy of models that are used to predict a target variable.

The F-score measures the harmonic mean of precision and recall, which are two other commonly used performance metrics in classification. The precision of a model refers to the proportion of true positive predictions in relation to all positive predictions. The recall of a model refers to the proportion of true positive predictions in relation to all actual positive instances.

The F-score combines both precision and recall to provide a single measure of model performance. This measure is particularly useful when data is imbalanced, meaning there are significantly more instances of one class than the other. In these situations, accuracy may not be an adequate measure of model performance.

The F-score ranges from 0 to 1, with 1 being the best possible score and 0 being the worst. A higher F-score indicates higher precision and recall, and therefore better overall model performance.