  Machine learning has revolutionized many fields of science and technology.
Through the $k$-Nearest Neighbors algorithm, we develop a model-independent
classifier, where the algorithm can classify phases of a model to which it has
never had access. For this, we study three distinct spin-$1$ models with some
common phases: the XXZ chains with uniaxial single-ion-type anisotropy, the
bound alternating XXZ chains, and the bilinear biquadratic chain. We show that,
with high probability, algorithms trained with two of these models can
determine common phases with the third. It is the first step toward a universal
classifier, where an algorithm is able to detect any phase with no knowledge
about the Hamiltonian, only knowing partial information about the quantum
state.
