Although machine learning has shown enormous successes in the geosciences, the lack of a proper uncertainty description of the machine learning results hampers acceptance in science and industry. We developed the theory for the first complete quantification of uncertainty, including a practical implementation method. The method is tested on toy problems and a real-world application to machine-learn the highly nonlinear process of the conversion of cloud droplets to rain droplets.