  Still under debate is the question of whether machine learning is capable of
going beyond black-box modeling for complex physical systems. We investigate
the generalizing and interpretability properties of learning algorithms. To
this end, we use supervised and unsupervised learning to infer the phase
boundaries of the active Ising model, starting from an ensemble of
configurations of the system. We illustrate that unsupervised learning
techniques are powerful at identifying the phase boundaries in the control
parameter space, even in situations of phase coexistence. It is demonstrated
that supervised learning with neural networks is capable of learning the
characteristics of the phase diagram, such that the knowledge obtained at a
limited set of control variables can be used to determine the phase boundaries
across the phase diagram. In this way, we show that properly designed
supervised learning provides predictive power to regions in the phase diagram
that are not included in the training phase of the algorithm. We stress the
importance of introducing interpretability methods in order to perform a
physically relevant classification of the phases with deep learning.
