Worst-case design is used in IoT devices and high performance data centers to ensure reliability, leading to a power efficiency loss. Recently, approximate computing has been proposed to trade off accuracy for efficiency. In this paper, we use Inexact Speculative Adders, which redesign the adder architecture to shorten its critical path and improve performance, but introduces controlled structural errors. On the other hand, overclocking is used to reduce conservative timing guardbands but could normally introduce catastrophic timing errors, we thus apply a supervised learning model to overclock speculative adders and predict their timing errors. We build a methodology to combine both structural and timing errors and analyze how they interplay with each other to limit the overal errors.