I have 350 examples that were annotated with binary labels by two different annotators. How do I compute inter-annotator agreement, including confidence interval?

To compute inter - annot ator agreement , you can use a variety of metrics such as Fle iss ' k appa , Scott ' s pi , or Cohen ' s k appa . These metrics measure the degree of agreement between two or more an not ators on a categor ical task such as label ing examples . \n \n To compute inter - annot ator agreement with a confidence interval , you can use a statistical method such as the Mc N em ar test or the Co ch ran ' s Q test . These tests can provide a p - value and a confidence interval for the observed agreement between the an not ators . \n \n Here ' s an example of how you can compute inter - annot ator agreement with a confidence interval using Fle iss ' k appa : \n \n 1 . First , you need to calculate the observed agreement between the two an not ators . You can do this by counting the number of times the an not ators agreed and disag reed on each example , and divid ing by the total number of examples . \n 2 . Next , you need to calculate the expected agreement between the two an not ators if they were randomly label ing the examples . You can do this by multip lying the marginal prob abilities of each label for each an not ator , and sum ming over all possible pairs of labels . \n 3 . Finally , you can calculate Fle iss ' k appa by sub tract ing the expected agreement from the observed agreement , divid ing by 1 - the expected agreement , and multip lying by 4 . Fle iss ' k appa ranges from - 1 to 1 , where - 1 indicates no agreement , 0 indicates agreement no better than chance , and 1 indicates perfect agreement . \n 4 . To compute a confidence interval for Fle iss ' k appa , you can use the Mc N em ar test . The Mc N em ar test provides a p - value and a confidence interval for the observed agreement between the two an not ators . You can use this p - value and confidence interval to determine whether the observed agreement is statist ically significant . \n \n Here ' s some sample Python code that demonstr ates how to compute inter - annot ator agreement with a confidence interval using Fle iss ' k appa and the Mc N em ar test : \n `` ` python \n import numpy as np \n from sc ip y . stats import m cn em ar \n \n # Example data \n labels = np . array ([ 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 1 , 0 , 1 , 0 ,