Before revision:  Based on a few observations, the method is learned and tailored to discriminate the various important plane regions a diagram is set into. With this tool one can automatically augment any sort of machine learning problem with access to a TDA method, enhance performances, construct features reflecting underlying changes in topological behaviour. The proposed methodology comes with only high level tuning parameters such as the encoding budget for topological features. We provide an open-access, ready-to-use implementation and notebook. We showcase the strengths and versatility of our approach on a number of applications .
After revision:  Based on a few observations, the method is learned and tailored to discriminate the various important plane regions where meaningful differences arise. We showcase the strength and robustness of our approach on a number of applications .