Objective: We present a random forest (RF) classification and regression technique to predict, intraoperatively, the Unified Parkinson’s Disease Rating Scale (UPDRS) improvement after Deep Brain Stimulation (DBS). We hypothesized that a data-informed combination of features extracted from intraoperative microelectrode recordings (MERs) can predict the motor improvement of Parkinson’s disease patients undergoing DBS surgery. Methods and Results: We modified the employed RFs to account for unbalanced data sets and multiple observations per patient, and showed, for the first time, that only 5 neurophysiologically interpretable MER signal features are sufficient for predicting UPDRS improvement. Conclusion and Significance: This finding suggests that STN electrophysiological signal characteristics are strongly correlated to the extent of motor behavior improvement observed in STNDBS.