BACKGROUND

A machine learning application may output a result and also a confidence score, which indicates the confidence that the machine learning application thinks the result is correct. However, the score often does not provide much value to a user. For example, the user may still not know what to do with the output after seeing the confidence score.

In one use case, a machine learning application may classify documents into two classes. The machine learning application may classify each document and provide a percentage score about the confidence of the classification, such as a document #1 is classified in a class #1 with an 80% confidence, and a document #2 is classified in a class #2 with a 55% confidence. A user can review the results and can ascertain that document #1 is being classified with a higher confidence than document #2. However, the user still has to individually decide what confidence is sufficient for not reviewing the output again. For example, in some cases, an 80% confidence can mean the result is correct; however, in other cases, the 80% confidence may mean the result is wrong. The only thing the user can ascertain is that document #1 was classified with a higher confidence than document #2, but the user cannot determine whether or not the user needs to review the output again. This results in an inefficient review process and lowers the value of using the machine learning application.

