Another measure for computing the performance for classification problems is estimating the precision, recall, and accuracy of the model.
Precision is defined as the number of true positives present in the mixture all retrieved instances:
Recall is the number of true positives identified from the total number of true positives present in all relevant documents:
Accuracy measures the percentage of closeness of the measured value from the standard value:
Fake document detection is a real-world use case that could explain this. For fake news detector systems, precision is the number of relevant fake news articles detected from the total number of documents that are detected. Recall, on the other hand, measures the number of fake news articles that get retrieved from the total number of fake news present. Accuracy measures the correctness with which such a system detects fake news. The following diagram shows the fake detector system:
