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The problem of identifying the phase of a given system for a certain value ofthe temperature can be reformulated as a classification problem in MachineLearning. Taking as a prototype the Ising model and using the Support VectorMachine as a tool to classify Monte Carlo generated configurations, we showthat the critical region of the system can be clearly identified and thesymmetry that drives the transition can be reconstructed from the performanceof the learning process. The role of the discrete symmetry of the system inobtaining this result is discussed. A finite size analysis of the learnedSupport Vector Machine decision function allows us to determine the criticaltemperature and critical exponents with a precision that is comparable to thatof the most efficient numerical approaches relying on a known Hamiltoniandescription of the system. For the determination of the critical temperatureand of the critical exponent connected with the divergence of the correlationlength, other than the availability of a range of temperatures havinginformation on both phases, the method we propose does not rest on any physicalinput on the system, and in particular is agnostic to its Hamiltonian, itssymmetry properties and its order parameter. Hence, our investigation providesa first significant step in the direction of devising robust tools forquantitative analyses of phase transitions in cases in which an order parameteris not known.

Machine Learning as a universal tool for quantitative investigations of phase transition