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 the Fle iss ' K appa coefficient , which is a measure of the agreement between two annot ators . It takes into account the agreement that would be expected by chance . \n \n Here ' s how you can compute the Fle iss ' K appa coefficient using Python : \n ``` python \n from sk learn . met rics import fle iss _ kappa \n \n # Assuming your annot ated examples are stored in a list called ' examples ' \n # and the binary labels are stored in a list called ' labels ' \n \n # Comput e the Fle iss ' K appa coefficient \n kappa = fle iss _ kappa ( labels , labels , average = None ) \n \n # Print the K appa coefficient \n print (" F le iss ' K appa coefficient : ", k appa ) \n ``` \n The ` f le iss _ kappa ` function from the ` sk learn . met rics ` module comput es the Fle iss ' K appa coefficient based on the observed agreement between the two annot ators . The ` labels ` parameter should contain the binary labels for each example , and the ` labels ` parameter should contain the binary labels for each annot ator . The ` aver age ` parameter can be set to ` None ` to compute the K appa coefficient without taking the average of the annot ators ' labels . \n \n To compute the confidence interval for the K appa coefficient , you can use the ` conf int ` function from the ` sci py . stats ` module . Here ' s an example : \n ``` python \n from sci py . stats import chi 2 \n \n # Assuming your annot ated examples are stored in a list called ' examples ' \n # and the binary labels are stored in a list called ' labels ' \n \n # Comput e the Fle iss ' K appa coefficient \n kappa = fle iss _ kappa ( labels , labels , average = None ) \n \n # Comput e the confidence interval for the K appa coefficient \n conf _ int = chi 2 . interval ( df = 2 *( len ( examples )- 1 ), loc = kappa , scale = 1 ) \n \n # Print the confidence interval \n print (" Conf idence interval : ", conf _ int ) \n ``` \n The ` conf int ` function comput es the confidence interval for the K appa coefficient based on the degrees of freedom (` df `) and the confidence level . In this example , the degrees of freedom is set to ` 2 *( len ( examples )- 1 )` to account for the number of annot ators and the number of examples . The confidence level can be set to any value between 0 and 1 , with higher values corresponding to a more confident interval . \n \n Note that the Fle iss ' K appa coefficient can range from - 1 to 1 , where 1 indicates perfect agreement , 0 indicates agreement no better than chance , and - 1 indicates agreement worse than chance . The confidence interval provides a range of values for the true K appa coefficient , which can help you interpret the results . </s>