F-score

Accuracy is a common metric for assessing the performance of binary classification models. However, it can sometimes be a difficult metric to interpret properly in the case where the number of examples of different classes is highly unbalanced. This is where the F-score, also known as the F1-score comes in; it combines precision and recall into a single score to provide a balanced measure of a model's accuracy.

It is calculated as the harmonic mean of precision and recall., and it balances the trade-off between precision (the ratio of true positives to all predicted positives) and recall (the ratio of true positives to all actual positives). This balance is important when dealing with situations where one metric may be favoured over the other.

The formula for calculating the F-score is:

The F-score ranges between 0 and 1, with higher values indicating better model performance.

It is particularly useful when you want to strike a balance between precision and recall, such as in information retrieval, medical diagnoses, or fraud detection, where false positives and false negatives have different consequences.