Depending on the endpoint, Rosette may return a confidence score, a salience score, a raw score, a match score, or some combination of scores. What are these scores measuring?
The dictionary defines confidence as "the quality or state of being certain". In machine learning, confidence defines the probability of the event. In the context of Rosette, Confidence is a measure of how correct Rosette believes its response to be. It attempts to answer the question: How sure am I that this answer is the right answer?
In the context of Rosette, Salience is a measure of how relevant the output is to the overall content of the input text. It attempts to answers the question: Does this analysis or information matter? It may be completely correct, but not of interest to the problem being evaluated. Salience refers to the relevance of the response to the overall input text.
Some endpoints return raw scores, which are normalized into confidence scores. Confidence scores are values between 0 and 1, while raw scores may be outside of that range.
The name processing functions sometimes use the term match score instead of confidence scores, as they pertain to how well names match when compared. The match scores are also values between 0 and 1, and provide a similar functionality as confidence scores.