We present in this paper the system sub-missions of the SDL Language Weaver team in the WMT 2012 Quality Estimation shared-task. Our MT quality-prediction systems use machine learning techniques (M5P regression-tree and SVM-regression models) and a feature-selection algorithm that has been designed to directly optimize towards the official metrics used in this shared-task. The resulting submissions placed 1st (the M5P model) and 2nd (the SVM model), respectively, on both the Ranking task and the Scoring task, out of 11 participating teams.