In this paper we discuss how the band 000000Swan uses machine learning to parse complex sensor data and create intricate artistic systems for live performance. Using the Wekinator software for interactive machine learning, we have created discrete and continuous models for controlling audio and visual environments using human gestures sensed by a commercially-available sensor bow and the Microsoft Kinect. In particular, we have employed machine learning to quickly and easily prototype complex relationships between performer gesture and performative outcome.