If the defined metric thresholds don’t feasibly exist anywhere in the domain, this feature will perform unpredictably. SigOpt does not know beforehand what is feasible, so setting unrealistic goals hinders our ability to exploit the information we gain to optimize towards the threshold you’ve specified. For example, if the accuracy threshold for a machine learning model is set to
984, when the actual accuracy exists between
0 and 1, this feature will assume the value
984 is actually achievable and explore the domain erratically trying to find solutions that satisfy this threshold.