In the XGBoost classifier example here the accuracy is calculated as:
accuracy = 1. - results["eval"]["error"][-1]
So, the last (
-1) element in the list
['error'] is being selected as the final error. There are 10 elements in the list. What do these 10 elements represent? I assume the last one is taken because there are 10 iterations (perhaps) and the last element represents the final iteration. Where can I find documentation to help interpret what is happening here? If there are 10 ‘iterations’, does each represent the error after a 10th of the training has occurred?