  The search for materials with topological properties is an ongoing effort. In
this article we propose a systematic statistical method supported by machine
learning techniques that is capable of constructing topological models for a
generic lattice without prior knowledge of the phase diagram. By sampling
tight-binding parameter vectors from a random distribution we obtain data sets
that we label with the corresponding topological index. This labeled data is
then analyzed to extract those parameters most relevant for the topological
classification and to find their most likely values. We find that the marginal
distributions of the parameters already define a topological model. Additional
information is hidden in correlations between parameters. Here we present as a
proof of concept the prediction of the Haldane model as the prototypical
topological insulator for the honeycomb lattice in Altland-Zirnbauer (AZ) class
A. The algorithm is straightforwardly applicable to any other AZ class or
lattice and could be generalized to interacting systems.
