Machine Learning with Swift

Precision, recall, and F1-score

To assess the quality of the algorithm considering the two types of error, accuracy metric is useless. That's why different metrics were proposed.

Precision and recall are metrics used to evaluate a prediction's quality in information retrieval and binary classification. Precision is a proportion of true positives among all predicted positives. It shows how relevant results are. Recall, also known as sensitivity, is a proportion of true positives among all truly positive samples. For example, if the task is to distinguish cat photos from non-cat photos, precision is a fraction of correctly predicted cats to all predicted cats. Recall is a fraction of predicted cats to the total number of true cats.

If we denote ...