Evaluation Metrics | Example code and own notes while taking the course "Intro to Machine Learning" on Udacity.
accuracy = number of items in a class labeled correctly / all items in that class
recall = true positive / true positive + false negative
Out of all the items that are truly positive, how many were correctly classified as positive. Or simply, how many positive items were "recalled" from the dataset.
precision = true positive / true positive + false positive
Out of all the items labeled as positive, how many truly belong to the positive class.
There is a good visual explanation about precision and recall on Wikipedia:
