  With the recent developments in machine learning, Carrasquilla and Melko have
proposed a paradigm that is complementary to the conventional approach for the
study of spin models. As an alternative to investigating the thermal average of
macroscopic physical quantities, they have used the spin configurations for the
classification of the disordered and ordered phases of a phase transition
through machine learning. We extend and generalize this method. We focus on the
configuration of the long-range correlation function instead of the spin
configuration itself, which enables us to provide the same treatment to
multi-component systems and the systems with a vector order parameter. We
analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition with the same
technique to classify three phases: the disordered, the BKT, and the ordered
phases. We also present the classification of a model using the training data
of a different model.
