  In this article we apply the random forest machine learning model to classify
1D topological phases when strong disorder is present. We show that using the
entanglement spectrum as training features the model gives high classification
accuracy. The trained model can be extended to other regions in phase space,
and even to other symmetry classes on which it was not trained and still
provides accurate results. After performing a detailed analysis of the trained
model we find that its dominant classification criteria captures degeneracy in
the entanglement spectrum.
