Function f1_score

pub fn f1_score<T: Numeric>(precision: T, recall: T) -> T

Computes the F-1 score of the Precision and Recall

2 * (precision * recall) / (precision + recall)

F-1 score

This is a harmonic mean of the two, which penalises the score more heavily if either the precision or recall are poor than an arithmetic mean.

The F-1 score is a helpful metric for assessing classifiers, as it takes into account that classes may be heavily biased which Accuracy does not. For example, it may be quite easy to create a 95% accurate test for a medical condition, which inuitively seems very good, but if 99.9% of patients are expected to not have the condition then accuracy is a poor way to measure performance because it does not consider that the cost of false negatives is very high.

Note that Precision and Recall both depend on there being a positive and negative class for a classification task, in some contexts this may be an arbitrary choice.

Precision

In classification, precision is true positives / positive predictions. It measures correct identifications of the positive class compared to all predictions of the positive class. You can trivially get 100% precision by never predicting the positive class, as this can never result in a false positive.

Note that the meaning of precision in classification or document retrieval is not the same as its meaning in measurements.

Recall

In classification, recall is true positives / actual positives. It measures how many of the positive cases are identified. You can trivially get 100% recall by always predicting the positive class, as this can never result in a false negative.

F scores

The F-1 score is an evenly weighted combination of Precision and Recall. For domains where the cost of false positives and false negatives are not equal, you should use a biased F score that weights precision or recall more strongly than the other.